Methods and compositions for the detection, classification, and diagnosis of schizophrenia

ABSTRACT

Disclosed are compositions and methods for the diagnosis and classification of schizophrenia.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/840,806, filed on Aug. 31, 2015 which claims the benefit of U.S.Provisional Application No. 62/043,871, filed on Aug. 29, 2014, each ofwhich is incorporated herein by reference in its entirety.

I. BACKGROUND

Patients with metal disorders may receive the same diagnosis, and yetshare few symptoms in common, vary widely in severity, and responddifferently to treatments. Genetic association studies of mentaldisorders were plagued by weak and inconsistent findings, largely as aresult of the clinical and etiologic heterogeneity of the cases whenpeople were described only as having the disorder or not (cases vscontrols). Classifications based on clinical features without regard formeasured genotypic differences also failed to predict response totreatment.

A disorder is “complex” when it is influenced by the combined effects ofinteracting genes. Individual genes do not consistently cause a mentaldisorder; rather, it takes many genes operating in concert, possiblyinteracting with specific environmental factors, in order for a personto develop mental illness. Complex diseases, such as schizophrenia, maybe influenced by hundreds or thousands of genetic variants that interactwith one another in complex ways, and consequently display amultifaceted genetic architecture. The genetic architecture of heritablediseases refers to the number, frequency, and effect sizes of geneticrisk alleles and the way they are organized into genotypic networks. Incomplex disorders, the same genotypic networks may lead to differentclinical outcomes (a concept known as multifinality, which is calledpleiotropy in genetics), and different genotypic networks may lead tothe same clinical outcome (equifinality, which is also described asheterogeneity). In general, geneticists must expect the likelihood thatmany genes affect each trait and each gene affects many traits.Consequently, research on complex heritable disorders like schizophreniais likely to yield weak and inconsistent results unless the complexityof their genetic and phenotypic architecture is taken into account.

For example, twin and family studies of schizophrenia consistentlyindicate that the variability in risk of disease is highly heritable(81%), but only 25% of the variability has been explained by specificgenetic variants identified in genome-wide association studies (GWAS).This is not surprising for complex disorders like schizophrenia becausecurrent GWAS methods have been unable to characterize the gene-geneinteractions (FIG. 1A) that influence the developing clinical profiles(FIG. 1B) in complex ways. The frequent failure to account for most ofthe heritability of complex disorders has been called the “missing” or“hidden” heritability problem.

In past studies of schizophrenia, the missing heritability problem hasbeen approached by analyzing the explained variance in large individualsamples or by using meta-analysis to combine data sets. Efforts havealso been made to consider the impact of variation related to ethnicity,sex, chromosomes, functional observations, or allele frequency.Nevertheless, most of the heritability of schizophrenia remainsunexplained. What is needed are new diagnostic methods that look at boththe genetic and phenotypic characteristic of schizophrenia and tools forthe performance and analysis of such methods.

II. SUMMARY

Disclosed are methods and compositions related to diagnosing, assessingthe risk, and classifying a subject with schizophrenia.

In one aspect, disclosed herein are diagnostic systems for diagnosingschizophrenia, wherein the diagnostic system comprises one or moreexpression panels, wherein the one or more expression panels eachcomprise one or more of the single nucleotide polymorphism (SNP) setscomprising 19_2, 88_64, 81_13, 87_76, 58_29, 83_41, 9_9, 10_4, 14_6,56_30, 42_37, 65_25, 71_55, 12_11, 90_78, 77_5, 88_8, 51_28, 59_48,41_12, 22_11, 13_12, 31_22, 85_84, 87_84, 16_10, 56_19, 75_31, 81_73,85_23, 21_8, 76_74, 61_39, 75_67, 76_63, 81_3, 87_26, 88_43, 25_10,12_2, 52_42, and/or 54_51.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “severe process, with positive and negativesymptom schizophrenia”, and wherein the one or more SNP sets comprise56_30, 75_67, and/or 76_74.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “positive and negative symptom Schizophrenia”,and wherein the one or more SNP sets comprise 59_48, 71_55, 21_8, 54_51,31_22, 65_25, and/or 87_84.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “negative Schizophrenia”, and wherein the one ormore SNP sets comprise 58_29, 9_9, 22_11, 81_3, 13_12, 61_39, 10_4,81_73, 75_31, 56_19, 88_8, and/or 12_2.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “Positive Schizophrenia”, and wherein the one ormore SNP sets comprise 88_64, 85_84, and/or 41_12.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “severe process, positive schizophrenia”, andwherein the one or more SNP sets comprise 77_5, 81_13, and/or 25_10.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “moderate process, disorganized negativeschizophrenia”, and wherein the one or more SNP sets comprise 19_2,52_42, 90_78, 12_11, 87_76, and/or 14_6.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “moderate process, positive and negativeschizophrenia”, and wherein the one or more SNP sets comprise 42_37,88_43, and/or 51_28.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “moderate process, continuous positiveschizophrenia”, and wherein the one or more SNP sets comprise 16_10,83_41, and/or 87_26.

Also disclosed herein are diagnostic systems of the invention, furthercomprising one or more phenotype panels, wherein each phenotype panelcomprises one or more phenotypic sets selected from the group comprising15_13, 12_11, 21_1, 50_46, 9_6, 46_23, 54_11, 30_17, 18_13, 27_6, 61_18,64_11, 65_64, 12_4, 42_9, 52_28, 7_3, 48_41, 26_8, 69_41, 10_5, 17_2,63_24, 69_66, 22_13, 53_6, 59_41, 20_19, 55_7, 34_17, 27_7, 4_1, 66_54,8_4, 51_38, 42_7, 18_3, 46_29, 5_2, 57_39, 11_5, 24_4, 48_7, 28_23,and/or 25_20.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “severe process, with positive and negativesymptom schizophrenia”, and wherein the one or more phenotypic setscomprise 15_13, 12_11, 21_1, 50_46, 9_6, 46_23, 54_11, 30_17, 18_13,27_6, 61_18, 64_11, and/or 65_64.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “positive and negative schizophrenia”, andwherein the one or more phenotypic sets comprise 12_4 and/or 42_9.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “negative schizophrenia”, and wherein the one ormore phenotypic sets comprise 52_28, 7_3, 48_41, 26_8, 69_41, 10_5,and/or 17_2.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “positive schizophrenia”, and wherein the one ormore phenotypic sets comprise 63_24 and/or 69_66.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “severe process, positive schizophrenia”, andwherein the one or more phenotypic sets comprise 22_13, 18_13, 53_6,59_41, 20_19, 55_7, 34_17, 69_66, 27_7, 18_13, 4_1, 66_54, and/or 8_4.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “moderate process, disorganized negativeschizophrenia”, and wherein the one or more phenotypic sets comprise51_38, 42_7, 18_3, and/or 46_29.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “moderate process, positive and negativeschizophrenia”, and wherein the one or more phenotypic sets comprise5_2, 57_39, 11_5, and/or 24_4.

Also disclosed is the diagnostic system of any preceding aspect, whereinthe system selects for “moderate process, continuous positiveschizophrenia”, and wherein the one or more phenotypic sets comprise48_7, 28_23, and/or 25_20.

Also disclosed is the diagnostic system of any preceding aspect, furthercomprising a means for reading the one or more expression panels, acomputer operationally linked to the means for reading the one or moreexpression panels, and a display for visualizing the diagnostic risk;wherein the computer identifies the expression profile of an expressionpanel, compares the expression profile to a control, and catalogs thatdata, wherein the computer provides an input source for inputtingphenotypic into a phenomic database; wherein the computer compares theexpression and phenomic data and calculates relationships between thegenomic and phenotypic data; wherein the computer compares the genomicand phenotypic relationship data to a reference standard; and whereinthe computer outputs the relationship data and the standard on thedisplay.

In one aspect, disclosed herein are methods of diagnosing a subject withschizophrenia comprising obtaining a biological sample from the subject,obtaining clinical data from the subject, and applying the biologicalsample and clinical data to the diagnostic system of any precedingaspect.

In one aspect, disclosed herein are methods of diagnosing a subject withschizophrenia and determining the schizophrenia class comprising:obtaining a biological sample from the subject; obtaining clinical datafrom the subject; applying the biological sample and clinical data to adiagnostic system for diagnosing schizophrenia, wherein the diagnosticsystem comprises one or more expression panels and one or morephenotypic panels; comparing the genomic and phenotypic panels resultsto a reference standard; wherein the presence of one or more SNP setsand phenotypic sets in the subjects sample indicates the presence ofschizophrenia, and wherein the genomic and phenotypic profile of thereference standard most closely correlating with the subjects genomicand phenotypic profile indicates schizophrenia class of the subject.

III. BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application with color drawing(s)will be provided by the Office by request and payment of the necessaryfee.

FIG. 1 shows the perception and visualization of a Genome-WideAssociation Study (GWAS). Panel A is a matrix corresponding to thegenome-wide association data set utilized in this work: GeneticAssociation Information Network (GAIN) and non-GAIN schizophreniasamples of the Molecular Genetics of Schizophrenia study. Allele valuesare indicated as BB (dark blue), AB (intermediate blue), AA (lightblue), and missing (black). Panel B is a matrix corresponding to thedistinct phenotypic consequences using data at the symptom level fromthe Diagnostic Interview for Genetic Studies corresponding to the GWASin panel A (see FIG. 2). Values are indicated as present (garnet),absent (salmon), and missing (black). Panel C presents schematics of the“divide and conquer” approach, in which natural partitions of GWAS data(identified as sets of interacting single-nucleotide polymorphisms[SNPs] or SNP sets) were cross-matched with decomposed schizophreniaphenotype (identified as clusters of naturally occurring schizophreniasymptoms or phenotypic sets), revealing a specific and distributedgenotypic-phenotypic architecture (networks of SNPs associated with setsof schizophrenia symptoms). This complex architecture is “invisible” or“hidden” to traditional GWAS.

FIG. 2 shows the methodology workflow of the divide & conquer strategy.Processes involving SNP and phenotypic sets are indicated in blue andred, respectively, whereas procedures concerning phenotypic-genotypicrelations are shown in violet. Statistical analysis was performed by theSNP-Set Kernel Association Test (SKAT), which is also accessible via theweb server cited above.

FIG. 3 shows examples of Identified Single-Nucleotide Polymorphism (SNP)Sets Represented as Heat Map Submatrices and their Corresponding Risk.Allele values are indicated as BB (dark blue), AB (intermediate blue),AA (light blue), and missing (black). Subject status (i.e., cases andcontrols) was superimposed after SNP set identification: cases in redand controls in green. Genotypic SNP sets are labeled by a pair ofnumbers representing the maximum number of clusters and the order inwhich they were selected by the method. All SNP sets are calculated withthe generalized factorization method based on the non-negative matrixfactorization method. Dendrograms were artificially superimposed forvisualization purposes. (See FIG. 4 for all SNP sets at more than 70% ofrisk.) Panels A-F illustrate SNP sets, representing submatrices of theoriginal genome-wide association study matrix and composed of sharedSNPs and/or subjects. Panel A presents a SNP set exhibiting ahomogeneous configuration in which all subjects in that group share thesame interaction among a specific set of homozygotic alleles (i.e.,SNP.times . . . times.SNP interactions). Panel B presents a SNP setencoding subjects exhibiting a particular heterozygotic genotype withrespect to the A allele in a subset of SNPs and another heterozygotegenotype with respect to the B allele in a different subset of SNPs(i.e., AND-type of interactions). Panel C presents a SNP set composed ofsubjects who share a particular genotype value for a subset of SNPs, andanother subset of subjects sharing a different genotype value for thesame subset of SNPs (i.e., OR-type of interactions). Inclusion-typerelations are exemplified by a SNP set (panel A) subsumed under a moregeneral SNP set (panel C), and both sets provide different descriptionsof target subjects. Panels D-F present SNP sets that combine allprevious interactions into more complex structures. Panel G presents asurface representing the risk function of the uncovered SNP sets. Therisk (z-axis; red=high, blue=low) was calculated based on thedistribution subject status (i.e., cases and controls) within each SNPset, and the surface was plotted interpolating the relation domains.Dendrograms reflect the order adopted for plotting SNP sets. SNP setswere clustered by shared SNP (x-axis) and by shared subjects (y-axis)using hypergeometric statistics. (Close-located SNP sets in an edgeshare more SNPs and/or subjects than those located far away.)

FIG. 4 shows SNP Sets represented as submatrices composed of SNPs(y-axis) shared by distinct subsets of subjects (x-axis). Allele valuesare indicated as AA (light blue), AB (intermediate blue), BB (darkblue), and missing (black). SNP and subject names/codes are not shown.Subject status was superimposed after SNP set identification: cases(red) and controls (green). SNP sets are labeled by a pair of numbersrepresenting the maximum number of sub-matrices and the order in whichthey were selected by the method, as described in FIG. 3. Row and columndendograms were superimposed a posteriori into each sub-matrix forvisualization purposes.

FIGS. 5A and 5B show dissection of a Genome-Wide Association Study(GWAS) and Identification of the Genotypic and Phenotypic Architectureof Schizophrenia. FIG. 5A presents a genotypic network, in which nodesindicate SNP sets linked by shared SNPs (blue lines) and/or subjects(red lines). The risk value, which was incorporated after the SNP setidentification, was color-coded. The 42 SNP sets harboring.gtoreq.70% ofrisk were topologically organized into 17 disjoint subnetworks. Subsetsof implicated genes are indicated. Highly connected SNP sets based onshared SNPs (blue lines) and subjects (red lines) might share aphenotypic profile (e.g., 81_13 and 88_64; see Table 7). Yet a super-SNPset, such as 81_13, may have unique—in addition to common—descriptivephenotypic features (see Table 7). Disconnected SNP sets, such as 71_55and 14_6, belong to disjoint networks that may include the same gene(i.e., NTKR3; see Table 2 and FIG. 6B but carry SNPs that are located indifferent regions of that gene, such as the promoter and coding regions,respectively. Both SNPs may produce distinct molecular consequences (seeTable 4 and FIG. 6B) and phenotypic profiles (see Table 7). FIG. 5Bshows the classes of schizophrenia mapped to the disease architecture(see Table 7). Eight classes of schizophrenia were identified byindependently characterizing each phenotypic feature included in agenotypic-phenotypic relationship; classifying each item based on thesymptoms as purely positive, purely negative, primarily positive, orprimarily negative symptoms; and clustering these relationships based ontheir recoded phenotypic domain using non-negative matrix factorization.SNP sets harboring only positive symptoms are indicated in green,whereas those displaying negative symptoms are in red. Intermediatecombinations including severe and/or moderate processes combined withpositive and/or negative and/or disorganized symptoms were alsocolor-coded. Dashed lines indicate nonsignificant matching.

FIG. 6 shows the bioinformatics analysis of SNPs derived from SNP Setstargeting genomic regions. (A) Multiple SNPs within a SNP set can affecta single gene in many ways. 5 SNPs from the SNP set 19_2 (100% of risk)can affect GOLGA1: SNPs rs10986471 and rs640052 may produce downstreamvariations; SNP rs634710 can generate missense variations; SNP rs7031479may introduce intron variants; and SNP rs687434 may create non-codingexon variants (Tables 2 and 4). Two SNP variants of the SNP set 19_2affect the regulatory region of ncRNAs genes: miRNA AL354928.1 and smallnuclear RNA (U4 snRNA) (Table 2). The rs640052 SNP lies betweenregulatory regions downstream and upstream of U4 and the GOLGA1 gene,which may be functionally related. The U4 snRNAs conform the splicesome,which is involved in the splicing process that generates diverse mRNAspecies from a single pre-mRNA. Consistently, the GOLGA1 gene hassubstantial variation in alternative splice isoform expression andalternative polyadenylation in cerebellar cortex between normalindividuals and SZ patients. (B) All SNPs from SNP set 7_55 are locatedin the intergenic region upstream of the NTRK3 gene, in the location ofa predicted enhancer (Table 2). Nevertheless, those SNPs of the 14_6 SNPset are located within NTRK3, principally in intronic regions and withinthe upstream region of pseudogene RP11-356B18.1 (Table 2). The latterpseudogene is harbored in an intron of NTRK3 that is processed in theNTRK-005 transcript variant, which does not code neurotrophin receptor-3protein. This suggests that a mutation in the first SNP set may inhibitthe transcription of the corresponding gene, whereas mutations in thesecond SNP set may block or decrease production of the correspondingprotein (Table 4). The protein coding genes include the 5′ and 3′untranslated region (3′UTR, 5° UTR), exons that code for the codingsequence (CDS) and introns. The ncRNA genes are defined only in terms ofexons and introns. The promoter upstream and downstream region for bothtypes of genes have been defined as the segment of 5000 bp before thebeginning of the 5′ UTR, and 5000 bp after the 3′UTR end. The remainingspace between the upstream and downstream region of a gene is heredefined as the intergenic region.

FIG. 7 shows a pathway analysis. Distinct pathways identified by the SNPsets are well known, relevant and interconnected signaling pathways forneural development, neurotrophin function, neurotransmission, andneurodegenerative disorders (see Tables 2 and 6). Other genes uncoveredare also overwhelmingly expressed in the brain, and participate inregulation of intracellular signaling, oxidative stress, apoptosis,neuroimmune regulation, protein synthesis, and epigenetic geneexpression.

IV. DETAILED DESCRIPTION

Before the present compounds, compositions, articles, devices, and/ormethods are disclosed and described, it is to be understood that theyare not limited to specific synthetic methods or specific recombinantbiotechnology methods unless otherwise specified, or to particularreagents unless otherwise specified, as such may, of course, vary. It isalso to be understood that the terminology used herein is for thepurpose of describing particular embodiments only and is not intended tobe limiting.

A. DEFINITIONS

As used in the specification and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise. Thus, for example, reference to “a pharmaceuticalcarrier” includes mixtures of two or more such carriers, and the like.

Ranges can be expressed herein as from “about” one particular value,and/or to “about” another particular value. When such a range isexpressed, another embodiment includes from the one particular valueand/or to the other particular value. Similarly, when values areexpressed as approximations, by use of the antecedent “about,” it willbe understood that the particular value forms another embodiment. Itwill be further understood that the endpoints of each of the ranges aresignificant both in relation to the other endpoint, and independently ofthe other endpoint. It is also understood that there are a number ofvalues disclosed herein, and that each value is also herein disclosed as“about” that particular value in addition to the value itself. Forexample, if the value “10” is disclosed, then “about 10” is alsodisclosed. It is also understood that when a value is disclosed that“less than or equal to” the value, “greater than or equal to the value”and possible ranges between values are also disclosed, as appropriatelyunderstood by the skilled artisan. For example, if the value “10” isdisclosed the “less than or equal to 10” as well as “greater than orequal to 10” is also disclosed. It is also understood that thethroughout the application, data is provided in a number of differentformats, and that this data, represents endpoints and starting points,and ranges for any combination of the data points. For example, if aparticular data point “10” and a particular data point 15 are disclosed,it is understood that greater than, greater than or equal to, less than,less than or equal to, and equal to 10 and 15 are considered disclosedas well as between 10 and 15. It is also understood that each unitbetween two particular units are also disclosed. For example, if 10 and15 are disclosed, then 11, 12, 13, and 14 are also disclosed.

In this specification and in the claims which follow, reference will bemade to a number of terms which shall be defined to have the followingmeanings:

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where said event or circumstance occurs and instances where itdoes not.

B. COMPOSITIONS

Throughout this application, various publications are referenced. Thedisclosures of these publications in their entireties are herebyincorporated by reference into this application in order to more fullydescribe the state of the art to which this pertains. The referencesdisclosed are also individually and specifically incorporated byreference herein for the material contained in them that is discussed inthe sentence in which the reference is relied upon.

We have chosen to measure and characterize the complexity of both thegenotypic and the phenotypic architecture of schizophrenia (FIG. 1C).Past studies have generally ignored variation in clinical features,categorizing people as either having or not having schizophrenia, andthey have looked only at the average effects of genetic variants,ignoring their organization into interactive genotypic networks. We showherein that schizophrenia heritability is not missing but is distributedinto different networks of interacting genes that influence differentpeople. Unlike previous studies that neglected clinical heterogeneityamong subjects with schizophrenia, we characterized the clinicalphenotype in detail. We also allowed for possible developmentalcomplexity, including equifinality (or heterogeneity) and multifinality(or pleiotropy).

We investigated the architecture of schizophrenia in the MolecularGenetics of Schizophrenia (MGS) study, in which all subjects hadconsistent and detailed genotypic and phenotypic assessments. We thenreplicated the results in two other independent samples in whichcomparable genotypic and phenotypic features were available: theClinical Antipsychotic Trial of Intervention Effectiveness (CATIE) andthe Portuguese Island studies from the Psychiatric Genomics Consortium(PGC).

The result of this work is a diagnostic system that is able to diagnosea subject as having schizophrenia, but more importantly classify thecategory of schizophrenia with which the subject is suffering. Toaccomplish this, the diagnostic system can comprise an expression panelthat can be used to detect nucleic acid or protein expression. Thus, inone aspect, disclosed herein are diagnostic systems for diagnosingschizophrenia, wherein the diagnostic system comprises one or moreexpression panels, wherein the one or more expression panels cancomprise one or more one or more expression sets (such as, for example,one or more SNP sets).

The expression panels disclosed herein can be assayed by any means tomeasure differential expression of a gene or protein known in the art.Specifically contemplated herein are methods of assessing the risk,diagnosing, or classifying schizophrenia comprising performing an assaythat measures differential expression of a nucleic acid, gene, peptide,or protein. Specifically contemplated are methods of assessing the risk,diagnosing, or classifying schizophrenia comprising performing an assaythat measures differential gene or protein expression, wherein the assayis selected from the group of assays comprising Northern analysis, RNAseprotection assay, PCR, QPCR, genome microarray, DNA microarray,MMCHipslow density PCR array, oligo array, protein array, peptide array,phenotype microarray, SAGE, and/or high throughput sequencing.Therefore, it is understood that the microarray panel can measuredifferential expression of a phenotypes, proteins, peptides, RNAs,microRNAs, DNAs, Single Nucleotide Polymorphisms (SNPs), or genes orsets of said phenotypes, proteins, peptides, RNAs, microRNAs, DNAs,Single Nucleotide Polymorphisms (SNPs), or genes. For example, in oneaspect, the disclosed panel can be a microarray such as a thosedeveloped and sold by Affymetrix, Agilent, Applied Microarrays, Arrayit,and IIlumina

In one aspect, the panel can comprise Single Nucleotide Polymorphism(SNP) sets. The SNP set can be any SNP set that has a greater than 70%association with risk for schizophrenia, including but not limited to19_2, 88_64, 81_13, 87_76, 58_29, 83_41, 9_9, 10_4, 14_6, 56_30, 42_37,65_25, 71_55, 12_11, 90_78, 77_5, 88_8, 51_28, 59_48, 41_12, 22_11,13_12, 31_22, 85_84, 87_84, 16_10, 56_19, 75_31, 81_73, 85_23, 21_8,76_74, 61_39, 75_67, 76_63, 81_3, 87_26, 88_43, 25_10, 12_2, 52_42, and54_51, which are specifically listed in Table 1.

TABLE 1 Single-Nucleotide Polymorphism (SNP) Sets Reported With ≥70%Risk of Schizophrenia, Statistical Comparison With Individual SNPs andCompositions ^(a) SKAT p Values SNP set Group Average SNP Best SNP WorstSNP Subjects (N) SNPs (N) Risk (%) 19_2 2.88E−05 3.43E−02 4.60E−041.38E−02 9 9 100 88_64 1.43E−11 2.06E−03 2.15E−07 1.79E−02 176 6 9681_13 1.46E−10 5.44E−03 2.15E−07 3.70E−02 234 10 95 87_76 7.11E−071.05E−02 1.37E−05 3.13E−02 74 3 95 58_29 5.41E−04 6.52E−03 2.07E−042.83E−02 125 6 94 83_41 3.87E−05 1.56E−04 1.01E−04 2.68E−04 61 4 93  9_91.51E−06 2.52E−03 1.23E−04 1.18E−02 144 19 92 10_4 3.83E−05 1.72E−022.11E−04 1.05E−02 58 11 91 14_6 2.38E−06 1.85E−03 1.23E−04 5.87E−03 2211 90 56_30 1.91E−10 4.33E−03 2.15E−07 2.10E−02 382 11 88 42_37 4.15E−062.35E−02 6.59E−05 1.38E−02 70 24 86 65_25 3.95E−05 1.99E−02 2.53E−048.83E−02 62 5 86 71_55 1.90E−05 3.99E−04 2.63E−05 1.08E−03 63 6 86 12_116.53E−04 2.28E−02 7.34E−03 1.05E−01 94 11 84 90_78 7.87E−04 2.99E−023.58E−02 9.53E−02 200 4 83 77_5 4.86E−05 5.01E−04 2.08E−05 1.49E−03 2975 82 88_8 2.88E−04 2.95E−02 3.58E−02 8.36E−02 32 10 82 51_28 2.07E−042.25E−02 1.75E−02 3.13E−02 258 3 81 59_48 2.32E−09 9.48E−03 2.38E−052.96E−02 174 7 80 41_12 1.36E−03 1.62E−02 1.12E−01 2.17E−02 78 3 7622_11 6.24E−05 4.29E−04 1.33E−04 1.08E−03 97 12 75 13_12 4.52E−053.61E−04 5.88E−05 1.45E−03 148 10 75 31_22 1.01E−04 2.37E−04 1.11E−044.03E−04 92 7 74 85_84 1.53E−05 1.01E−04 1.37E−05 1.81E−04 39 4 74 87_841.19E−04 1.40E−02 1.37E−05 1.30E−02 22 13 74 16_10 1.81E−03 1.59E−022.92E−03 5.92E−02 141 12 73 56_19 2.02E−04 6.69E−04 1.02E−04 1.76E−03 905 73 75_31 2.61E−05 1.37E−02 1.02E−04 9.53E−02 197 8 73 81_73 1.13E−052.99E−02 2.57E−04 1.29E−02 213 10 73 85_23 6.20E−03 9.46E−03 5.58E−031.16E−02 53 4 73 21_8 6.24E−05 4.29E−04 l.33E−04 1.08E−03 188 12 7176_74 1.58E−17 1.33E−02 1.12E−05 1.17E−02 284 14 71 61_39 1.04E−032.43E−02 1.90E−03 5.45E−02 51 3 71 75_67 3.76E−18 7.16E−02 2.15E−071.00E−03 877 32 71 76_63 2.07E−02 2.25E−02 1.75E−02 3.13E−02 34 3 7181_3 6.24E−05 4.29E−04 1.33E−04 1.08E−03 107 12 71 87_26 2.49E−036.03E−03 4.14E−03 1.12E−02 28 5 71 88_43 1.37E−04 1.85E−03 6.03E−044.82E−03 70 7 71 25_10 3.49E−06 1.67E−03 1.11E−04 1.53E−02 124 9 70 12_21.81E−03 1.59E−02 2.92E−04 5.92E−02 194 12 70 52_42 5.70E−05 5.06E−036.59E−05 3.60E−02 87 16 70 54_51 1.49E−05 5.01E−04 2.08E−04 1.49E−03 1325 70 ^(a) SKAT = SNP-Set Kernel Association Test.

Accordingly, in one aspect, disclosed herein are diagnostic systems fordiagnosing schizophrenia, wherein the diagnostic system comprises one ormore expression panels, wherein the one or more expression panels eachcomprise one or more of the single nucleotide polymorphism (SNP) setsselected from the group comprising, but not limited to 19_2, 88_64,81_13, 87_76, 58_29, 83_41, 9_9, 10_4, 14_6, 56_30, 42_37, 65_25, 71_55,12_11, 90_78, 77_5, 88_8, 51_28, 59_48, 41_12, 22_11, 13_12, 31_22,85_84, 87_84, 16_10, 56_19, 75_31, 81_73, 85_23, 21_8, 76_74, 61_39,75_67, 76_63, 81_3, 87_26, 88_43, 25_10, 12_2, 52_42, and/or 54_51. Itis understood and herein contemplated that each of the SNP setsdisclosed herein maps to one or more nucleic acid molecules. Therefore,a single SNP set will not necessarily be comprised solely of primers orprobes for detection of a single SNP, but can be comprised of multipleprimers and probes for the detection of SNPs mapping to at least one,two, three, four, five, six, seven, eight, nine, ten, eleven, twelve,thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, ortwenty nucleic acid locations. As disclosed in Table 2, each of the SNPsets disclosed herein maps to particular locations on a gene, includingprotein coding and non-coding regulatory variants.

TABLE 2 Mapping SNP sets into genomic information. (Information obtainedfrom HaploReg v2, dbSNP and NCBI databases) dbSNP func- NCBI GWAS NCBIassociation to Group Chr Gene tion annotation Neuronal Functionassociation to SZ other CNS disorders Summary  9_9 15 NTRK3 intronicneurotrophic tyrosine kinase, receptor, Yes This gene encodes a memberof the neurotrophic type 3 tyrosine receptor kinase (NTRK) family. Thiskinase is a membrane-bound receptor that, upon neurotrophin binding,phosphorylates itself and members of the MAPK pathway. Signallingthrough this kinase leads to cell differentiation and may play a role inthe development of proprioceptive neurons that sense body position.Mutations in this gene have been associated with medulloblastomas,secretory breast carcinomas and other cancers. Several transcriptvariants encoding different isoforms have been found for this gene  9_97 SEMA3A intronic regulation of axonal growth Yes This gene is a memberof the semaphorin family and encodes a protein with an Ig-like C2-type(immunoglobulin-like) domain, a PSI domain and a Sema domain. Thissecreted protein can function as either a chemorepulsive agent,inhibiting axonal outgrowth, or as a chemoattractive agent, stimulatingthe growth of apical dendrites. In both cases, the protein is vital fornormal neuronal pattern development. Increased expression of thisprotein is associated with schizophrenia and is seen in a variety ofhuman tumor cell lines. Also, aberrant release of this protein isassociated with the progression of Alzheimer's disease. 10_4 14C14orf102 intronic mRNA suppression yes NRDE-2, necessary for RNAinterference, domain (autism and ADHD) containing 10_4 14 C14orf102(5′)mRNA suppression yes NRDE-2, necessary for RNA interference, domain(autism and ADHD) containing 10_4 14 PSMC1 intronic Ubiquitin dependentATPase, yes The 26S proteasome is a multicatalytic proteinase NFkBpathway (Spinocerebellar atrophy 7) complex with a highly orderedstructure composed of 2 complexes, a 20S core and a 19S regulator. The20S core is composed of 4 rings of 28 non- identical subunits; 2 ringsare composed of 7 alpha subunits and 2 rings are composed of 7 betasubunits. The 19S regulator is composed of a base, which contains 6ATPase subunits and 2 non- ATPase subunits, and a lid, which contains upto 10 non-ATPase subunits. Proteasomes are distributed throughouteukaryotic cells at a high concentration and cleave peptides in anATP/ubiquitin-dependent process in a non-lysosomal pathway. An essentialfunction of a modified proteasome, the immunoproteasome, is theprocessing of class I MHC peptides. This gene encodes one of the ATPasesubunits, a member of the triple-A family of ATPases which have achaperone-like activity. This subunit and a 20S core alpha subunitinteract specifically with the hepatitis B virus X protein, a proteincritical to viral replication. This subunit also interacts with theadenovirus E1A protein and this interaction alters the activity of theproteasome. Finally, this subunit interacts with ataxin-7, suggesting arole for the proteasome in the development of Spinocerebellar ataxiatype 7, a progressive neurodegenerative disorder. 10_4 14 PSMC1(3′)Ubiquitin dependent ATPase, yes The 26S proteasome is a multicatalyticproteinase NFkB pathway (Spinocerebellar atrophy 7) complex with ahighly ordered structure composed of 2 complexes, a 20S core and a 19Sregulator. The 20S core is composed of 4 rings of 28 non- identicalsubunits; 2 rings are composed of 7 alpha subunits and 2 rings arecomposed of 7 beta subunits. The 19S regulator is composed of a base,which contains 6 ATPase subunits and 2 non- ATPase subunits, and a lid,which contains up to 10 non-ATPase subunits. Proteasomes are distributedthroughout eukaryotic cells at a high concentration and cleave peptidesin an ATP/ubiquitin-dependent process in a non-lysosomal pathway. Anessential function of a modified proteasome, the immunoproteasome, isthe processing of class I MHC peptides. This gene encodes one of theATPase subunits, a member of the triple-A family of ATPases which have achaperone-like activity. This subunit and a 20S core alpha subunitinteract specifically with the hepatitis B virus X protein, a proteincritical to viral replication. This subunit also interacts with theadenovirus E1A protein and this interaction alters the activity of theproteasome. Finally, this subunit interacts with ataxin-7, suggesting arole for the proteasome in the development of Spinocerebellar ataxiatype 7, a progressive neurodegenerative disorder. 10_4 14 PSMC1(5′)Ubiquitin dependent ATPase, yes The 26S proteasome is a multicatalyticproteinase NFkB pathway (Spinocerebellar atrophy 7) complex with ahighly ordered structure composed of 2 complexes, a 20S core and a 19Sregulator. The 20S core is composed of 4 rings of 28 non-identicalsubunits; 2 rings are composed of 7 alpha subunits and 2 rings arecomposed of 7 beta subunits. The 19S regulator is composed of a base,which contains 6 ATPase subunits and 2 non-ATPase subunits, and a lid,which contains up to 10 non-ATPase subunits. Proteasomes are distributedthroughout eukaryotic cells at a high concentration and cleave peptidesin an ATP/ubiquitin-dependent process in a non- lysosomal pathway. Anessential function of a modified proteasome, the immunoproteasome, isthe processing of class I MHC peptides. This gene encodes one of theATPase subunits, a member of the triple-A family of ATPases which have achaperone-like activity. This subunit and a 20S core alpha subunitinteract specifically with the hepatitis B virus X protein, a proteincritical to viral replication. This subunit also interacts with theadenovirus E1A protein and this interaction alters the activity of theproteasome. Finally, this subunit interacts with ataxin-7, suggesting arole for the proteasome in the development of spinocerebellar ataxiatype 7, a progressive neurodegenerative disorder. 12_11 14 C14orf102intronic mRNA suppression yes NRDE-2, necessary for RNA interference,domain (autism and ADHD) containing 12_11 14 C14orf102(5′) mRNAsuppression yes NRDE-2, necessary for RNA interference, domain (autismand ADHD) containing 12_11 14 PSMC1 intronic Ubiquitin dependent ATPase,yes The 26S proteasome is a multicatalytic proteinase NFkB pathway(Spinocerebellar atrophy 7) complex with a highly ordered structurecomposed of 2 complexes, a 20S core and a 19S regulator. The 20S core iscomposed of 4 rings of 28 non-identical subunits; 2 rings are composedof 7 alpha subunits and 2 rings are composed of 7 beta subunits. The 19Sregulator is composed of a base, which contains 6 ATPase subunits and 2non-ATPase subunits, and a lid, which contains up to 10 non-ATPasesubunits. Proteasomes are distributed throughout eukaryotic cells at ahigh concentration and cleave peptides in an ATP/ubiquitin-dependentprocess in a non- lysosomal pathway. An essential function of a modifiedproteasome, the immunoproteasome, is the processing of class I MHCpeptides. This gene encodes one of the ATPase subunits, a member of thetriple-A family of ATPases which have a chaperone-like activity. Thissubunit and a 20S core alpha subunit interact specifically with thehepatitis B virus X protein, a protein critical to viral replication.This subunit also interacts with the adenovirus E1A protein and thisinteraction alters the activity of the proteasome. Finally, this subunitinteracts with ataxin-7, suggesting a role for the proteasome in thedevelopment of spinocerebellar ataxia type 7, a progressiveneurodegenerative disorder. 12_11 14 PSMC1(3′) Ubiquitin dependentATPase, yes The 26S proteasome is a multicatalytic proteinase NFkBpathway (Spinocerebellar atrophy 7) complex with a highly orderedstructure composed of 2 complexes, a 20S core and a 19S regulator. The20S core is composed of 4 rings of 28 non-identical subunits; 2 ringsare composed of 7 alpha subunits and 2 rings are composed of 7 betasubunits. The 19S regulator is composed of a base, which contains 6ATPase subunits and 2 non-ATPase subunits, and a lid, which contains upto 10 non-ATPase subunits. Proteasomes are distributed throughouteukaryotic cells at a high concentration and cleave peptides in anATP/ubiquitin-dependent process in a non- lysosomal pathway. Anessential function of a modified proteasome, the immunoproteasome, isthe processing of class I MHC peptides. This gene encodes one of theATPase subunits, a member of the triple-A family of ATPases which have achaperone-like activity. This subunit and a 20S core alpha subunitinteract specifically with the hepatitis B virus X protein, a proteincritical to viral replication. This subunit also interacts with theadenovirus E1A protein and this interaction alters the activity of theproteasome. Finally, this subunit interacts with ataxin-7, suggesting arole for the proteasome in the development of spinocerebellar ataxiatype 7, a progressive neurodegenerative disorder. 12_11 14 PSMC1(5′)Ubiquitin dependent ATPase, yes The 26S proteasome is a multicatalyticproteinase NFkB pathway (Spinocerebellar atrophy 7) complex with ahighly ordered structure composed of 2 complexes, a 20S core and a 19Sregulator. The 20S core is composed of 4 rings of 28 non-identicalsubunits; 2 rings are composed of 7 alpha subunits and 2 rings arecomposed of 7 beta subunits. The 19S regulator is composed of a base,which contains 6 ATPase subunits and 2 non-ATPase subunits, and a lid,which contains up to 10 non-ATPase subunits. Proteasomes are distributedthroughout eukaryotic cells at a high concentration and cleave peptidesin an ATP/ubiquitin-dependent process in a non- lysosomal pathway. Anessential function of a modified proteasome, the immunoproteasome, isthe processing of class I MHC peptides. This gene encodes one of theATPase subunits, a member of the triple-A family of ATPases which have achaperone-like activity. This subunit and a 20S core alpha subunitinteract specifically with the hepatitis B virus X protein, a proteincritical to viral replication. This subunit also interacts with theadenovirus E1A protein and this interaction alters the activity of theproteasome. Finally, this subunit interacts with ataxin-7, suggesting arole for the proteasome in the development of spinocerebellar ataxiatype 7, a progressive neurodegenerative disorder. 12_2 4 HPGDS 3′-UTRprostaglandin D synthase Yes Prostaglandin-D synthase is a sigma classglutathione-S-transferase family member. The enzyme catalyzes theconversion of PGH2 to PGD2 and plays a role in the production ofprostanoids in the immune system and mast cells. The presence of thisenzyme can be used to identify the differentiation stage of humanmegakaryocytes. [provided by RefSeq, July 2008] 12_2 4 HPGDS intronicprostaglandin D synthase Yes Prostaglandin-D synthase is a sigma classglutathione-S-transferase family member. The enzyme catalyzes theconversion of PGH2 to PGD2 and plays a role in the production ofprostanoids in the immune system and mast cells. The presence of thisenzyme can be used to identify the differentiation stage of humanmegakaryocytes. 12_2 4 HPGDS(5′) prostaglandin D synthase YesProstaglandin-D synthase is a sigma class glutathione-S-transferasefamily member. The enzyme catalyzes the conversion of PGH2 to PGD2 andplays a role in the production of prostanoids in the immune system andmast cells. The presence of this enzyme can be used to identify thedifferentiation stage of human megakaryocytes. 12_2 4 RP11-363G15.2spliceosome complex activation no This gene encodes a component of thespliceosome (retinitis pigmentosa) complex and is one of severalretinitis pigmentosa- causing genes. When the gene product is added tothe spliceosome complex, activation occurs. 12_2 4 SMARCAD1 3′-UTRactin-dependent chromatin regulation Yes This gene encodes a member ofthe SNF subfamily of helicase proteins. The encoded protein plays acritical role in the restoration of heterochromatin organization andpropagation of epigenetic patterns following DNA replication bymediating histone H3/H4 deacetylation. Mutations in this gene areassociated with adermatoglyphia. Alternatively spliced transcriptvariants encoding multiple isoforms have been observed for this gene.12_2 4 SMARCAD1 intronic actin-dependent chromatin regulation Yes Thisgene encodes a member of the SNF subfamily of helicase proteins. Theencoded protein plays a critical role in the restoration ofheterochromatin organization and propagation of epigenetic patternsfollowing DNA replication by mediating histone H3/H4 deacetylation.Mutations in this gene are associated with adermatoglyphia.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. 12_2 4 SMARCAD1 missenseactin-dependent chromatin regulation Yes This gene encodes a member ofthe SNF subfamily of helicase proteins. The encoded protein plays acritical role in the restoration of heterochromatin organization andpropagation of epigenetic patterns following DNA replication bymediating histone H3/H4 deacetylation. Mutations in this gene areassociated with adermatoglyphia. Alternatively spliced transcriptvariants encoding multiple isoforms have been observed for this gene.12_2 4 SMARCAD1 synonymous actin-dependent chromatin regulation Yes Thisgene encodes a member of the SNF subfamily of helicase proteins. Theencoded protein plays a critical role in the restoration ofheterochromatin organization and propagation of epigenetic patternsfollowing DNA replication by mediating histone H3/H4 deacetylation.Mutations in this gene are associated with adermatoglyphia.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. 13_12 14 EML5 intronic WD40 domainprotein expressed in brain no echinoderm microtubule associated proteinlike 5 13_12 14 SPATA7 missense isolated in testis and retina no Thisgene, originally isolated from testis, is also (retinitis pigmentosa andexpressed in retina. Mutations in this gene are Lieber amaurosis)associated with Leber congenital amaurosis and juvenile retinitispigmentosa. Alternatively spliced transcript variants encoding differentisoforms have been found for this gene. 13_12 14 U4.15(3′) RNA, U4 smallnuclear 92, pseudogene? RNA, U4 small nuclear 1 13_12 14 U4.15(5′) RNA,U4 small nuclear 92, pseudogene? RNA, U4 small nuclear 2 13_12 14ZC3H14 * intronic mRNA stability, nuclear export, and yes ZC3H14 belongsto a family of poly(A)-binding translation (regulation of tau pathology)proteins that influence gene expression by regulating mRNA stability,nuclear export, and translation 14_6 15 NTRK3 intronic neurotrophictyrosine kinase, receptor, Yes This gene encodes a member of theneurotrophic type 3 tyrosine receptor kinase (NTRK) family. This kinaseis a membrane-bound receptor that, upon neurotrophin binding,phosphorylates itself and members of the MAPK pathway. Signallingthrough this kinase leads to cell differentiation and may play a role inthe development of proprioceptive neurons that sense body position.Mutations in this gene have been associated with medulloblastomas,secretory breast carcinomas and other cancers. Several transcriptvariants encoding different isoforms have been found for this gene 16_104 HPGDS 3′-UTR prostaglandin D synthase Yes Prostaglandin-D synthase isa sigma class glutathione-S-transferase family member. The enzymecatalyzes the conversion of PGH2 to PGD2 and plays a role in theproduction of prostanoids in the immune system and mast cells. Thepresence of this enzyme can be used to identify the differentiationstage of human megakaryocytes. 16_10 4 HPGDS intronic prostaglandin Dsynthase Yes Prostaglandin-D synthase is a sigma classglutathione-S-transferase family member. The enzyme catalyzes theconversion of PGH2 to PGD2 and plays a role in the production ofprostanoids in the immune system and mast cells. The presence of thisenzyme can be used to identify the differentiation stage of humanmegakaryocytes. 16_10 4 HPGDS(5′) prostaglandin D synthase YesProstaglandin-D synthase is a sigma class glutathione-S-transferasefamily member. The enzyme catalyzes the conversion of PGH2 to PGD2 andplays a role in the production of prostanoids in the immune system andmast cells. The presence of this enzyme can be used to identify thedifferentiation stage of human megakaryocytes. 16_10 4 RP11-363G15.2spliceosome complex activation No no This gene encodes a component ofthe spliceosome (retinitis pigmentosa) complex and is one of severalretinitis pigmentosa- causing genes. When the gene product is added tothe spliceosome complex, activation occurs. 16_10 4 SMARCAD1 3′-UTRactin-dependent chromatin regulation Yes This gene encodes a member ofthe SNF subfamily of helicase proteins. The encoded protein plays acritical role in the restoration of heterochromatin organization andpropagation of epigenetic patterns following DNA replication bymediating histone H3/H4 deacetylation. Mutations in this gene areassociated with adermatoglyphia. Alternatively spliced transcriptvariants encoding multiple isoforms have been observed for this gene.16_10 4 SMARCAD1 intronic actin-dependent chromatin regulation Yes Thisgene encodes a member of the SNF subfamily of helicase proteins. Theencoded protein plays a critical role in the restoration ofheterochromatin organization and propagation of epigenetic patternsfollowing DNA replication by mediating histone H3/H4 deacetylation.Mutations in this gene are associated with adermatoglyphia.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. 16_10 4 SMARCAD1 missenseactin-dependent chromatin regulation Yes This gene encodes a member ofthe SNF subfamily of helicase proteins. The encoded protein plays acritical role in the restoration of heterochromatin organization andpropagation of epigenetic patterns following DNA replication bymediating histone H3/H4 deacetylation. Mutations in this gene areassociated with adermatoglyphia. Alternatively spliced transcriptvariants encoding multiple isoforms have been observed for this gene.16_10 4 SMARCAD1 synonymous actin-dependent chromatin regulation YesThis gene encodes a member of the SNF subfamily of helicase proteins.The encoded protein plays a critical role in the restoration ofheterochromatin organization and propagation of epigenetic patternsfollowing DNA replication by mediating histone H3/H4 deacetylation.Mutations in this gene are associated with adermatoglyphia.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. 19_2 9 ARPC5L actin-binding protein noactin related protein 2/3 complex, subunit 5-like 19_2 9 ARPC5L intronicactin-binding protein no actin related protein 2/3 complex, subunit5-like 19_2 9 GOLGA1 golgi associated protein no The Golgi apparatus,which participates in glycosylation and transport of proteins and lipidsin the secretory pathway, consists of a series of stacked cisternae(flattened membrane sacs). Interactions between the Golgi andmicrotubules are thought to be important for the reorganization of theGolgi after it fragments during mitosis. This gene encodes one of thegolgins, a family of proteins localized to the Golgi. This encodedprotein is associated with Sjogren's syndrome. 19_2 9 GOLGA1 3′-UTRgolgi associated protein no The Golgi apparatus, which participates inglycosylation and transport of proteins and lipids in the secretorypathway, consists of a series of stacked cisternae (flattened membranesacs). Interactions between the Golgi and microtubules are thought to beimportant for the reorganization of the Golgi after it fragments duringmitosis. This gene encodes one of the golgins, a family of proteinslocalized to the Golgi. This encoded protein is associated withSjogren's syndrome. 19_2 9 GOLGA1 intronic golgi associated protein noThe Golgi apparatus, which participates in glycosylation and transportof proteins and lipids in the secretory pathway, consists of a series ofstacked cisternae (flattened membrane sacs). Interactions between theGolgi and microtubules are thought to be important for thereorganization of the Golgi after it fragments during mitosis. This geneencodes one of the golgins, a family of proteins localized to the Golgi.This encoded protein is associated with Sjogren's syndrome. 19_2 9GOLGA1 missense golgi associated protein no The Golgi apparatus, whichparticipates in glycosylation and transport of proteins and lipids inthe secretory pathway, consists of a series of stacked cisternae(flattened membrane sacs). Interactions between the Golgi andmicrotubules are thought to be important for the reorganization of theGolgi after it fragments during mitosis. This gene encodes one of thegolgins, a family of proteins localized to the Golgi. This encodedprotein is associated with Sjogren's syndrome. 19_2 9 GOLGA1 synonymousgolgi associated protein no The Golgi apparatus, which participates inglycosylation and transport of proteins and lipids in the secretorypathway, consists of a series of stacked cisternae (flattened membranesacs). Interactions between the Golgi and microtubules are thought to beimportant for the reorganization of the Golgi after it fragments duringmitosis. This gene encodes one of the golgins, a family of proteinslocalized to the Golgi. This encoded protein is associated withSjogren's syndrome. 19_2 9 RPL35 intronic ribosomal protein noRibosomes, the organelles that catalyze protein synthesis, consist of asmall 40S subunit and a large 60S subunit. Together these subunits arecomposed of 4 RNA species and approximately 80 structurally distinctproteins. This gene encodes a ribosomal protein that is a component ofthe 60S subunit. The protein belongs to the L29P family of ribosomalproteins. It is located in the cytoplasm. As is typical for genesencoding ribosomal proteins, there are multiple processed pseudogenes ofthis gene dispersed through the genome. 19_2 9 SCAI regulator of Raspathway of cell no his gene encodes a regulator of cell migration. Themigration encoded protein appears to function in the RhoA (ras homologgene family, member A)-Dia1 (diaphanous homolog 1) signal transductionpathway. Alternatively spliced transcript variants have been described.19_2 9 SCAI intronic regulator of Ras pathway of cell no his geneencodes a regulator of cell migration. The migration encoded proteinappears to function in the RhoA (ras homolog gene family, member A)-Dia1(diaphanous homolog 1) signal transduction pathway. Alternativelyspliced transcript variants have been described. 19_2 9 WDR38 intronicWD38 domain protein no WD repeat domain 38 21_8 2 AC068490.2 transcriptwithout known gene product 22_11 2 AC068490.2 transcript without knowngene product 25_10 X AL158819.7 (3′) * transfer RNA tanscript PAGE5.This gene is a member of the GAGE family, which is expressed in avariety of tumors and in some fetal and reproductive tissues. Theprotein encoded by this gene shares a sequence similarity with otherGAGE/PAGE proteins. It may also belong to a family of CT (cancer-testis)antigens. Multiple alternatively spliced transcript variants encodingdistinct isoforms have been found for this gene, but the biologicalvalidity of some variants have not been determined 25_10 X FOXR2 *missense carcinogenic transcription factor no forkhead box R2 25_10 XFOXR2(3′) * carcinogenic transcription factor no forkhead box R3 25_10 XMAGEH1(5′) * apoptosis mediator no This gene is thought to be involvedin apoptosis. Multiple polyadenylation sites have been found for thisgene. 25_10 X PAGE3 * none (prostate associated gene) no P antigenfamily, member 3 (prostate associated) 25_10 X PAGE3 * missense none(prostate associated gene) no P antigen family, member 3 (prostateassociated) 25_10 X PAGE3(3′) * none (prostate associated gene) no Pantigen family, member 3 (prostate associated) 25_10 X PAGE5(3′) *inhibition of apoptosis no P antigen family, member 3 (prostateassociated) 25_10 X PAGE5(5′) * inhibition of apoptosis no This gene isa member of the GAGE family, which is expressed in a variety of tumorsand in some fetal and reproductive tissues. The protein encoded by thisgene shares a sequence similarity with other GAGE/PAGE proteins. It mayalso belong to a family of CT (cancer-testis) antigens. Multiplealternatively spliced transcript variants encoding distinct isoformshave been found for this gene, but the biological validity of somevariants have not been determined. 25_10 X RP11-382F24.2 * transcriptwithout known gene product no 25_10 X RP11-382F24.2(3′) * transcriptwithout known gene product no 25_10 X RP11-382F24.2(5′) * transcriptwithout known gene product no 25_10 X RP13-188A5.1 * transcript withoutknown gene product no 25_10 X RRAGB intronic Ras related GTP binding noRas-homologous GTPases constitute a large family of signal transducersthat alternate between an activated, GTP-binding state and aninactivated, GDP-binding state. These proteins represent cellularswitches that are operated by GTP- exchange factors and factors thatstimulate their intrinsic GTPase activity. All GTPases of the Rassuperfamily have in common the presence of six conserved motifs involvedin GTP/GDP binding, three of which are phosphate-/magnesium-bindingsites (PM1-PM3) and three of which are guanine nucleotide-binding sites(G1-G3). Transcript variants encoding distinct isoforms have beenidentified. 25_10 X RRAGB(3′) Ras related GTP binding no Ras-homologousGTPases constitute a large family of signal transducers that alternatebetween an activated, GTP-binding state and an inactivated, GDP-bindingstate. These proteins represent cellular switches that are operated byGTP- exchange factors and factors that stimulate their intrinsic GTPaseactivity. All GTPases of the Ras superfamily have in common the presenceof six conserved motifs involved in GTP/GDP binding, three of which arephosphate-/magnesium-binding sites (PM1-PM3) and three of which areguanine nucleotide-binding sites (G1-G3). Transcript variants encodingdistinct isoforms have been identified. 25_10 X RRAGB(5′) Ras relatedGTP binding no Ras-homologous GTPases constitute a large family ofsignal transducers that alternate between an activated, GTP-bindingstate and an inactivated, GDP-binding state. These proteins representcellular switches that are operated by GTP- exchange factors and factorsthat stimulate their intrinsic GTPase activity. All GTPases of the Rassuperfamily have in common the presence of six conserved motifs involvedin GTP/GDP binding, three of which are phosphate-/magnesium-bindingsites (PM1-PM3) and three of which are guanine nucleotide-binding sites(G1-G3). Transcript variants encoding distinct isoforms have beenidentified. 25_10 X SNORD112.49(3′) * small nucleolar RNA with ribosomalno small nucleolar RNA, C/D box 112 function 31_22 6 C6orf138 3′-UTRunkown function yes patched domain 5 (smoking cessation) 31_22 6C6orf138 intronic unkown function yes patched domain 5 (smokingcessation) 31_22 6 C6orf138 synonymous unkown function yes patcheddomain 5 (smoking cessation) 31_22 6 C6orf138(3′) unkown function yespatched domain 6 (smoking cessation) 31_22 6 OPN5(3′) * neuropsin yesOpsins are members of the guanine nucleotide- (G protein associatedreceptor) (bipolar disorder) binding protein (G protein)-coupledreceptor superfamily. This opsin gene is expressed in the eye, brain,testes, and spinal cord. This gene belongs to the seven-exon subfamilyof mammalian opsin genes that includes peropsin (RRH) and retinal Gprotein coupled receptor (RGR). Like these other seven-exon opsin genes,this family member may encode a protein with photoisomerase activity.Alternative splicing results in multiple transcript variants. 41_12 XGPR119(3′) rhodopsin no This gene encodes a member of the rhodopsin (Gprotein associated receptor) subfamily of G-protein-coupled receptorsthat is expressed in the pancreas and gastrointestinal tract. Theencoded protein is activated by lipid amides includinglysophosphatidylcholine and oleoylethanolamide and may be involved inglucose homeostasis. This protein is a potential drug target in thetreatment of type 2 diabetes 41_12 X SLC25A14 intronic mitochondrialuncoupling in neurons but two other UCP genes Mitochondrial uncouplingproteins (UCP) are are associated to SZ members of the larger family ofmitochondrial anion carrier proteins (MACP). UCPs separate oxidativephosphorylation from ATP synthesis with energy dissipated as heat, alsoreferred to as the mitochondrial proton leak. UCPs facilitate thetransfer of anions from the inner to the outer mitochondrial membraneand the return transfer of protons from the outer to the innermitochondrial membrane. They also reduce the mitochondrial membranepotential in mammalian cells. Tissue specificity occurs for thedifferent UCPs and the exact methods of how UCPs transfer H+/OH− are notknown. UCPs contain the three homologous protein domains of MACPs. Thisgene is widely expressed in many tissues with the greatest abundance inbrain and testis 41_12 X SLC25A14(3′) mitochondrial uncoupling inneurons but two other UCP genes are Mitochondrial uncoupling proteins(UCP) are associated to SZ members of the larger family of mitochondrialanion carrier proteins (MACP). UCPs separate oxidative phosphorylationfrom ATP synthesis with energy dissipated as heat, also referred to asthe mitochondrial proton leak. UCPs facilitate the transfer of anionsfrom the inner to the outer mitochondrial membrane and the returntransfer of protons from the outer to the inner mitochondrial membrane.They also reduce the mitochondrial membrane potential in mammaliancells. Tissue specificity occurs for the different UCPs and the exactmethods of how UCPs transfer H+/OH− are not known. UCPs contain thethree homologous protein domains of MACPs. This gene is widely expressedin many tissues with the greatest abundance in brain and testis 42_37 11NCAM1 neuronal adhesion expression is abnormal in SCH. This gene encodesa cell adhesion protein which is a member of the immunoglobulinsuperfamily. The encoded protein is involved in cell-to-cellinteractions as well as cell-matrix interactions during development anddifferentiation. The encoded protein has been shown to be involved indevelopment of the nervous system, and for cells involved in theexpansion of T cells and dendritic cells which play an important role inimmune surveillance. Alternative splicing results in multiple transcriptvariants. 42_37 11 NCAM1 intronic neuronal adhesion expression isabnormal in SCH. This gene encodes a cell adhesion protein which is amember of the immunoglobulin superfamily. The encoded protein isinvolved in cell-to-cell interactions as well as cell-matrixinteractions during development and differentiation. The encoded proteinhas been shown to be involved in development of the nervous system, andfor cells involved in the expansion of T cells and dendritic cells whichplay an important role in immune surveillance. Alternative splicingresults in multiple transcript variants. 42_37 11 RP11-629G13.1 noveltranscript, antisense to NCAM1 expression is abnormal in SCH. 42_37 11RP11-629G13.1 intronic novel transcript, antisense to NCAM1 expressionis abnormal in SCH. 42_37 11 RP11-629G13.1(3′) novel transcript,antisense to NCAM1 expression is abnormal in SCH. 42_37 2 AC064837.1 *intronic Novel miRNA REAL GeneNAME IPP5: Protein phosphatase-1 (PP1) isa major serine/threonine phosphatase that regulates a variety ofcellular functions. PP1 consists of a catalytic subunit (see PPP1CA; MIM176875) and regulatory subunits that determine the subcellularlocalization of PP1 or regulate its function. PPP1R1C belongs to a groupof PP1 inhibitory subunits that are themselves regulated byphosphorylation 42_37 2 PPP1R1C intronic protein phosphatase 1,regulatory regulates TNF induced apoptosis REAL GeneNAME IPP5: Proteinphosphatase-1 (inhibitor) subunit (p53 mediated) (PP1) is a majorserine/threonine phosphatase that regulates a variety of cellularfunctions. PP1 consists of a catalytic subunit (see PPP1CA; MIM 176875)and regulatory subunits that determine the subcellular localization ofPP1 or regulate its function. PPP1R1C belongs to a group of PP1inhibitory subunits that are themselves regulated by phosphorylation51_28 X IGSF1 a member of the immunoglobulin- central hypothyroidism andThis gene encodes a member of the like domain-containing superfamilytesticular enlargement. immunoglobulin-like domain-containingsuperfamily. Proteins in this superfamily contain varying numbers ofimmunoglobulin-like domains and are thought to participate in theregulation of interactions between cells. Multiple transcript variantsencoding different isoforms have been found for this gene. 52_42 11NCAM1 neuronal adhesion expression is abnormal in SCH. This gene encodesa cell adhesion protein which is a member of the immunoglobulinsuperfamily. The encoded protein is involved in cell-to-cellinteractions as well as cell-matrix interactions during development anddifferentiation. The encoded protein has been shown to be involved indevelopment of the nervous system, and for cells involved in theexpansion of T cells and dendritic cells which play an important role inimmune surveillance. Alternative splicing results in multiple transcriptvariants. 52_42 11 NCAM1 intronic neuronal adhesion expression isabnormal in SCH. This gene encodes a cell adhesion protein which is amember of the immunoglobulin superfamily. The encoded protein isinvolved in cell-to-cell interactions as well as cell-matrixinteractions during development and differentiation. The encoded proteinhas been shown to be involved in development of the nervous system, andfor cells involved in the expansion of T cells and dendritic cells whichplay an important role in immune surveillance. Alternative splicingresults in multiple transcript variants. 52_42 11 RP11-629G13.1 noveltranscript, antisense to NCAM1 expression is abnormal in SCH. 52_42 11RP11-629G13.1 intronic novel transcript, antisense to NCAM1 expressionis abnormal in SCH. 52_42 11 RP11-629G13.1(3′) novel transcript,antisense to NCAM1 expression is abnormal in SCH. 54_51 8 CSMD1 intronicpotential tumor suppressor Yes deletion related to head and neck CUB andSushi multiple domains 1 carcinomas 56_19 11 SNX19(5′) * sorting nexin19 Yes sorting nexin 19 56_30 1 7SK.207(3′) * non coding RNA noveltranscript snRNA 56_30 1 7SK.207(5′) * non coding RNA novel transcriptsnRNA 56_30 1 PTBP2 intronic controls the assembly of other Yes Theprotein encoded by this gene binds to the splicing-regulatory proteinsintronic cluster of RNA regulatory elements, downstream control sequence(DCS). It is implicated in controlling the assembly of othersplicing-regulatory proteins. This protein is very similar to thepolypyrimidine tract binding protein but it is expressed primarily inthe brain. 56_30 1 PTBP2 synonymous controls the assembly of other YesThe protein encoded by this gene binds to the splicing-regulatoryproteins intronic cluster of RNA regulatory elements, downstream controlsequence (DCS). It is implicated in controlling the assembly of othersplicing-regulatory proteins. This protein is very similar to thepolypyrimidine tract binding protein but it is expressed primarily inthe brain. 56_30 1 PTBP2(5′) controls the assembly of other Yes Theprotein encoded by this gene binds to the splicing-regulatory proteinsintronic cluster of RNA regulatory elements, downstream control sequence(DCS). It is implicated in controlling the assembly of othersplicing-regulatory proteins. This protein is very similar to thepolypyrimidine tract binding protein but it is expressed primarily inthe brain. 56_30 1 RP4-726F1.1(3′) * non coding RNA novel transcriptRodopsine: Retinitis pigmentosa is an inherited progressive diseasewhich is a major cause of blindness in western communities. It can beinherited as an autosomal dominant, autosomal recessive, or X-linkedrecessive disorder. In the autosomal dominant form, which comprisesabout 25% of total cases, approximately 30% of families have mutationsin the gene encoding the rod photoreceptor-specific protein rhodopsin.This is the transmembrane protein which, when photoexcited, initiatesthe visual transduction cascade. Defects in this gene are also one ofthe causes of congenital stationary night blindness. 56_30 16 GP2 *intronic glycoprotein 2 Yes glycoprotein 2 (zymogen granule membrane)56_30 16 GP2 * synonymous glycoprotein 2 Yes glycoprotein 2 (zymogengranule membrane) 56_30 16 GP2(3′) * glycoprotein 2 Yes glycoprotein 2(zymogen granule membrane) 58_29 8 CTD-3025N20.2(3′) * Novel long noncoding RNA Genomic clone: CTD Coats disease 58_29 8 RP11-1D12.2(5′) *Novel long non coding RNA 59_48 20 RP11-128M1.1 Novel long non codingRNA 59_48 20 RP11-128M1.1(3′) Novel long non coding RNA 59_48 8TRPS1(3′) transcription factor that represses This gene encodes atranscription factor that GATA-regulated genes and binds repressesGATA-regulated genes and binds to a to a dynein light chain proteindynein light chain protein. Binding of the encoded protein to the dyneinlight chain protein affects binding to GATA consensus sequences andsuppresses its transcriptional activity. Defects in this gene are acause of tricho-rhino-phalangeal syndrome (TRPS) types I-III 61_39 XIGSF1 a member of the immunoglobulin- central hypothyroidism and Thisgene encodes a member of the like domain-containing superfamilytesticular enlargement. immunoglobulin-like domain-containingsuperfamily. Proteins in this superfamily contain varying numbers ofimmunoglobulin-like domains and are thought to participate in theregulation of interactions between cells. Multiple transcript variantsencoding different isoforms have been found for this gene. 65_25 20C20orf78(5′) * exon, codes protein of unknown function chromosome 20open reading frame 79 71_55 15 NTRK3(3′) * neurotrophic tyrosinereceptor kinase Yes alcoholism This gene encodes a member of theneurotrophic (NTRK) tyrosine receptor kinase (NTRK) family. This kinaseis a membrane-bound receptor that, upon neurotrophin binding,phosphorylates itself and members of the MAPK pathway. Signallingthrough this kinase leads to cell differentiation and may play a role inthe development of proprioceptive neurons that sense body position.Mutations in this gene have been associated with medulloblastomas,secretory breast carcinomas and other cancers. Several transcriptvariants encoding different isoforms have been found for this gene 75_311 AC093577.1 (3′) Novel non-coding miRNA genomic clone RELATED to FAM69family of cysteine-rich type II transmembrane proteins. These proteinslocalize to the endoplasmic reticulum but their specific functions areunknown. Alternatively spliced transcript variants encoding multipleisoforms have been observed for this gene. 75_31 1 AC093577.1 (5′) Novelnon-coding miRNA genomic clone RELATED to FAM69 family of cysteine-richtype II transmembrane proteins. These proteins localize to theendoplasmic reticulum but their specific functions are unknown.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. 75_31 1 U6.1077(5′) U6 spliceosomalRNA RNA, U6 small nuclear 75_31 11 SNX19(5′) * sorting nexin 19 Yessorting nexin 19 75_67 1 SNORA42.4 (5′) * small nucleolar RNA, H/ACA box42; small nucleolar RNA, H/ACA box 42 regulation of gene expression75_67 1 VANGL1(5′) * tretraspanin family member; NfKB This gene encodesa member of the tretraspanin regulating microRNA family. The encodedprotein may be involved in mediating intestinal trefoil factor inducedwound healing in the intestinal mucosa. Mutations in this gene areassociated with neural tube defects. Alternate splicing results inmultiple transcript variants. 75_67 10 RP11-298H24.1(3′) * Novel longnon coding RNA 75_67 12 STYK1 intronic Receptor protein tyrosine kinasesNOK/STYK1 interacts with GSK-3? Receptor protein tyrosine kinases, likeSTYK1, play and mediates Ser9 phosphorylation important roles in diversecellular and through activated Akt. developmental processes, such ascell proliferation, differentiation, and survival 75_67 14 AL161669.1(3′) * MicroRNA? 75_67 14 AL161669.1 (5′) * MicroRNA? 75_67 14AL161669.2 * MicroRNA 75_67 14 AL161669.2 (3′) * MicroRNA 75_67 155S_rRNA.496(3′) * 5S ribosomal RNA 5S ribosomal RNA 75_67 15 NTRK3(3′) *neurotrophic tyrosine receptor kinase Yes alcoholism This gene encodes amember of the neurotrophic (NTRK) tyrosine receptor kinase (NTRK)family. This kinase is a membrane-bound receptor that, upon neurotrophinbinding, phosphorylates itself and members of the MAPK pathway.Signalling through this kinase leads to cell differentiation and mayplay a role in the development of proprioceptive neurons that sense bodyposition. Mutations in this gene have been associated withmedulloblastomas, secretory breast carcinomas and other cancers. Severaltranscript variants encoding different isoforms have been found for thisgene 75_67 16 7SK.236(5′) * non coding RNA novel transcript snRNA 75_6716 GP2 * intronic glycoprotein 2 Yes glycoprotein 2 (zymogen granulemembrane) 75_67 16 GP2 * synonymous glycoprotein 2 Yes glycoprotein 2(zymogen granule membrane) 75_67 16 GP2(3′) * glycoprotein 2 Yesglycoprotein 2 (zymogen granule membrane) 75_67 22 CTA-714B7.5 Noveltranscript, genomic, unknown protein. PCYT1A phosphatecytidylyltransferase 1, choline, alpha 75_67 3 RP11-436A20.3 Novel longnon coding RNA Homo sapiens 3 BAC RP11-436A20 (Roswell Park CancerInstitute Human BAC Library) complete sequence. 75_67 4 C4orf37sperm-tail PG-rich repeat containing 2 sperm-tail PG-rich repeat 75_67 4C4orf37(3′) sperm-tail PG-rich repeat containing 3 sperm-tail PG-richrepeat 75_67 4 RP11-431J17.1(3′) Novel long non coding RNA Homo sapiensBAC clone RP11-431J17 from 4, complete sequence 75_67 8 7SK.7(3′) *snRNA 75_67 8 DKK4(5′) * a Wnt/beta catenin signaling pathway Yes geneexpression is altered This gene encodes a protein that is a member ofthe member of the dickkopf family in schizophrenia dickkopf family. Thesecreted protein contains two involved in embryonic development cysteinerich regions and is involved in embryonic development through itsinteractions with the Wnt signaling pathway. Activity of this protein ismodulated by binding to the Wnt co-receptor and the co-factor kremen 2.75_67 8 DUSP4(5′) * dual specificity phosphatase 4; Yes The proteinencoded by this gene is a member of gene product inactivates the dualspecificity protein phosphatase subfamily. ERK1, ERK2 and JNK Thesephosphatases inactivate their target kinases by dephosphorylating boththe phosphoserine/threonine and phosphotyrosine residues. Theynegatively regulate members of the mitogen-activated protein (MAP)kinase superfamily (MAPK/ERK, SAPK/JNK, p38), which are associated withcellular proliferation and differentiation. Different members of thefamily of dual specificity phosphatases show distinct substratespecificities for various MAP kinases, different tissue distribution andsubcellular localization, and different modes of inducibility of theirexpression by extracellular stimuli. This gene product inactivates ERK1,ERK2 and JNK, is expressed in a variety of tissues, and is localized inthe nucleus. Two alternatively spliced transcript variants, encodingdistinct isoforms, have been observed for this gene. In addition,multiple polyadenylation sites have been reported. 75_67 8 GSR intronicglutathione reductase Cerebrovascular disease, This gene encodes amember of the class-I pyridine metabolic syndrome nucleotide-disulfideoxidoreductase family. This enzyme is a homodimeric flavoprotein. It isa central enzyme of cellular antioxidant defense, and reduces oxidizedglutathione disulfide (GSSG) to the sulfhydryl form GSH, which is animportant cellular antioxidant. Rare mutations in this gene result inhereditary glutathione reductase deficiency. Multiple alternativelyspliced transcript variants encoding different isoforms have been found.75_67 8 RP11-401H2.1(5′) * exon transcript. Codes an unknown protein75_67 8 RP11-486M23.1(5′) * Novel long non coding RNA 75_67 8RP11-738G5.1(3′) * Novel long non coding RNA 75_67 8 RP11-770E5.1 Novelantisense gene transcript 75_67 8 SLC20A2 intronic Type 3sodium-dependent phosphate Mutations in this gene may play a This geneencodes a member of the inorganic symporter; confers susceptibility torole in familial idiopathic basal phosphate transporter family. Theencoded protein viral infection as a gamma-retroviral gangliacalcification is a type 3 sodium-dependent phosphate symporter receptor.that plays an important role in phosphate homeostasis by mediatingcellular phosphate uptake. The encoded protein also conferssusceptibility to viral infection as a gamma- retroviral receptor.Mutations in this gene may play a role in familial idiopathic basalganglia calcification. Alternatively spliced transcript variantsencoding multiple isoforms have been observed for this gene. 75_67 8SNTG1 intronic Syntrophins; mediates dystrophin binding. The proteinencoded by this gene is a member of Specifically expressed in the brainthe syntrophin family. Syntrophins are cytoplasmic peripheral membraneproteins that typically contain 2 pleckstrin homology (PH) domains, aPDZ domain that bisects the first PH domain, and a C- terminal domainthat mediates dystrophin binding. This gene is specifically expressed inthe brain. Transcript variants for this gene have been described, buttheir full-length nature has not been determined. 75_67 8 SNTG1(3′)Syntrophins; mediates dystrophin binding. The protein encoded by thisgene is a member of Specifically expressed in the brain the syntrophinfamily. Syntrophins are cytoplasmic peripheral membrane proteins thattypically contain 2 pleckstrin homology (PH) domains, a PDZ domain thatbisects the first PH domain, and a C- terminal domain that mediatesdystrophin binding. This gene is specifically expressed in the brain.Transcript variants for this gene have been described, but theirfull-length nature has not been determined. 75_67 8 ST18 intronicSuppression of tumorigenicity 18 suppression of tumorigenicity 18(breast carcinoma) (zinc finger protein); pro apoptotic (zinc fingerprotein) 75_67 8 VDAC3 * intronic voltage-dependent anion channel(VDAC), Cerebrovascular disease, This gene encodes a voltage-dependentanion and belongs to the mitochondrial metabolic syndrome channel(VDAC), and belongs to the mitochondrial porin family. Pro apoptoticporin family. VDACs are small, integral membrane proteins that traversethe outer mitochondrial membrane and conduct ATP and other smallmetabolites. They are known to bind several kinases of intermediarymetabolism, thought to be involved in translocation of adeninenucleotides, and are hypothesized to form part of the mitochondrialpermeability transition pore, which results in the release of cytochromec at the onset of apoptotic cell death. Alternatively transcriptvariants encoding different isoforms have been described for this gene.76_63 X IGSF1 a member of the immunoglobulin- central hypothyroidism andThis gene encodes a member of the like domain-containing superfamilytesticular enlargement. immunoglobulin-like domain-containingsuperfamily. Proteins in this superfamily contain varying numbers ofimmunoglobulin-like domains and are thought to participate in theregulation of interactions between cells. Multiple transcript variantsencoding different isoforms have been found for this gene. 76_74 14AL161669.1 (3′) * MicroRNA? 76_74 14 AL161669.1 (5′) * MicroRNA? 76_7414 AL161669.2 * MicroRNA 76_74 14 AL161669.2 (3′) * MicroRNA 76_74 16ABCC12(3′) ATP-binding cassette (ABC) transporters This gene is a memberof the superfamily of ATP- binding cassette (ABC) transporters and theencoded protein contains two ATP-binding domains and 12 transmembraneregions. ABC proteins transport various molecules across extra- andintracellular membranes. ABC genes are divided into seven distinctsubfamilies: ABC1, MDR/TAP, MRP, ALD, OABP, GCN20, and White. This geneis a member of the MRP subfamily which is involved in multi-drugresistance. This gene and another subfamily member are arrangedhead-to-tail on chromosome 16q12.1. Increased expression of this gene isassociated with breast cancer. 76_74 16 ITFG1 intronic Integrin alpha FGGAP repeat integrin alpha FG-GAP repeat containing 1 containing protein76_74 16 NETO2 * neuropilin (NRP) and tolloid (TLL)- rats encodes aprotein that This gene encodes a predicted transmembrane like 2modulates glutamate signaling protein containing two extracellular CUBdomains in the brain by regulating followed by a low-density lipoproteinclass A kainate receptor function. (LDLa) domain. A similar gene in ratsencodes a protein that modulates glutamate signaling in the brain byregulating kainate receptor function. Expression of this gene may be abiomarker for proliferating infantile hemangiomas. A pseudogene of thisgene is located on the long arm of chromosome 8. Alternatively splicedtranscript variants encoding multiple isoforms have been observed forthis gene. 76_74 16 NETO2 * intronic neuropilin (NRP) and tolloid (TLL)-rats encodes a protein that This gene encodes a predicted transmembranelike 2 modulates glutamate signaling protein containing twoextracellular CUB domains in the brain by regulating followed by alow-density lipoprotein class A kainate receptor function. (LDLa)domain. A similar gene in rats encodes a protein that modulatesglutamate signaling in the brain by regulating kainate receptorfunction. Expression of this gene may be a biomarker for proliferatinginfantile hemangiomas. A pseudogene of this gene is located on the longarm of chromosome 8. Alternatively spliced transcript variants encodingmultiple isoforms have been observed for this gene. 76_74 16 PHKB *intronic phosphorylase kinase, beta Phosphorylase kinase is a polymer of16 subunits, four each of alpha, beta, gamma and delta. The alphasubunit includes the skeletal muscle and hepatic isoforms, encoded bytwo different genes. The beta subunit is the same in both the muscle andhepatic isoforms, encoded by this gene, which is a member of thephosphorylase b kinase regulatory subunit family. The gamma subunit alsoincludes the skeletal muscle and hepatic isoforms, encoded by twodifferent genes. The delta subunit is a calmodulin and can be encoded bythree different genes. The gamma subunits contain the active site of theenzyme, whereas the alpha and beta subunits have regulatory functionscontrolled by phosphorylation. The delta subunit mediates the dependenceof the enzyme on calcium concentration. Mutations in this gene causeglycogen storage disease type 9B, also known as phosphorylase kinasedeficiency of liver and muscle. Alternatively spliced transcriptvariants encoding different isoforms have been identified in this gene.Two pseudogenes have been found on chromosomes 14 and 20, respectively76_74 16 PHKB * missense phosphorylase kinase, beta Phosphorylase kinaseis a polymer of 16 subunits, four each of alpha, beta, gamma and delta.The alpha subunit includes the skeletal muscle and hepatic isoforms,encoded by two different genes. The beta subunit is the same in both themuscle and hepatic isoforms, encoded by this gene, which is a member ofthe phosphorylase b kinase regulatory subunit family. The gamma subunitalso includes the skeletal muscle and hepatic isoforms, encoded by twodifferent genes. The delta subunit is a calmodulin and can be encoded bythree different genes. The gamma subunits contain the active site of theenzyme, whereas the alpha and beta subunits have regulatory functionscontrolled by phosphorylation. The delta subunit mediates the dependenceof the enzyme on calcium concentration. Mutations in this gene causeglycogen storage disease type 9B, also known as phosphorylase kinasedeficiency of liver and muscle. Alternatively spliced transcriptvariants encoding different isoforms have been identified in this gene.Two pseudogenes have been found on chromosomes 14 and 20, respectively76_74 16 PHKB(3′) * phosphorylase kinase, beta Phosphorylase kinase is apolymer of 16 subunits, four each of alpha, beta, gamma and delta. Thealpha subunit includes the skeletal muscle and hepatic isoforms, encodedby two different genes. The beta subunit is the same in both the muscleand hepatic isoforms, encoded by this gene, which is a member of thephosphorylase b kinase regulatory subunit family. The gamma subunit alsoincludes the skeletal muscle and hepatic isoforms, encoded by twodifferent genes. The delta subunit is a calmodulin and can be encoded bythree different genes. The gamma subunits contain the active site of theenzyme, whereas the alpha and beta subunits have regulatory functionscontrolled by phosphorylation. The delta subunit mediates the dependenceof the enzyme on calcium concentration. Mutations in this gene causeglycogen storage disease type 9B, also known as phosphorylase kinasedeficiency of liver and muscle. Alternatively spliced transcriptvariants encoding different isoforms have been identified in this gene.Two pseudogenes have been found on chromosomes 14 and 20, respectively76_74 4 C4orf37 sperm-tail PG-rich repeat containing 2 sperm-tailPG-rich repeat 76_74 4 C4orf37(3′) sperm-tail PG-rich repeat containing2 sperm-tail PG-rich repeat 76_74 4 RP11-431J17.1(3′) Novel long noncoding RNA Homo sapiens BAC clone RP11-431J17 from 4, complete sequence76_74 4 SOD3(5′) * superoxide dismutase (SOD) protein This gene encodesa member of the superoxide dismutase (SOD) protein family. SODs areantioxidant enzymes that catalyze the dismutation of two superoxideradicals into hydrogen peroxide and oxygen. The product of this gene isthought to protect the brain, lungs, and other tissues from oxidativestress. The protein is secreted into the extracellular space and forms aglycosylated homotetramer that is anchored to the extracellular matrix(ECM) and cell surfaces through an interaction with heparan sulfateproteoglycan and collagen. A fraction of the protein is cleaved near theC-terminus before secretion to generate circulating tetramers that donot interact with the ECM. [provided by RefSeq, July 2008] 76_74 5CTD-2292M14.1(3′) * non coding long RNA novel transcript Genomic clone:CTD Coats disease 76_74 8 RP11-1D12.2(5′) * Novel long non coding RNA76_74 8 RP11-770E5.1 Novel antisense gene transcript 77_5 8 CSMD1intronic potential tumor suppressor Yes deletion related to head CUB andSushi multiple domains 1 and neck carcinomas 81_13 16 GP2 * intronicglycoprotein 2 Yes glycoprotein 2 (zymogen granule membrane) 81_13 16GP2 * synonymous glycoprotein 2 Yes glycoprotein 2 (zymogen granulemembrane) 81_13 16 GP2(3′) * glycoprotein 2 Yes glycoprotein 2 (zymogengranule membrane) 81_13 8 RP11-401H2.1(5′) * exon transcript. Codes anunknown protein 81_13 8 SNTG1 intronic Syntrophins; mediates dystrophinbinding. The protein encoded by this gene is a member of Specificallyexpressed in the brain the syntrophin family. Syntrophins arecytoplasmic peripheral membrane proteins that typically contain 2pleckstrin homology (PH) domains, a PDZ domain that bisects the first PHdomain, and a C- terminal domain that mediates dystrophin binding. Thisgene is specifically expressed in the brain. Transcript variants forthis gene have been described, but their full-length nature has not beendetermined. [provided by RefSeq, July 2008] 81_13 8 SNTG1(3′)Syntrophins; mediates dystrophin binding. The protein encoded by thisgene is a member of Specifically expressed in the brain the syntrophinfamily. Syntrophins are cytoplasmic peripheral membrane proteins thattypically contain 2 pleckstrin homology (PH) domains, a PDZ domain thatbisects the first PH domain, and a C- terminal domain that mediatesdystrophin binding. This gene is specifically expressed in the brain.Transcript variants for this gene have been described, but theirfull-length nature has not been determined. [provided by RefSeq, July2008] 81_3 2 AC068490.2 transcript without known gene product 81_73 11TMEM135 intronic transmembrane protein Cerebrovascular disease,transmembrane protein 135 metabolic syndrome 81_73 11 TMEM135(3′)transmembrane protein Cerebrovascular disease, transmembrane protein 136metabolic syndrome 81_73 15 RYR3 intronic ryanodine receptor,Cerebrovascular disease, The protein encoded by this gene is a ryanodinemetabolic syndrome receptor, which functions to release calcium fromintracellular storage for use in many cellular processes. For example,the encoded protein is involved in skeletal muscle contraction byreleasing calcium from the sarcoplasmic reticulum followed bydepolarization of T-tubules. Two transcript variants encoding differentisoforms have been found for this gene 81_73 18 CHST9 introniccarbohydrate (N-acetylgalactosamine cell-cell interaction, signal Theprotein encoded by this gene belongs to the 4-0) sulfotransferase 9transduction, and embryonic sulfotransferase 2 family. It is localizedto the golgi development, expressed in membrane, and catalyzes thetransfer of sulfate to pituitary position 4 of non-reducingN-acetylgalactosamine (GalNAc) residues in both N-glycans and O-glycans. Sulfate groups on carbohydrates confer highly specificfunctions to glycoproteins, glycolipids, and proteoglycans, and arecritical for cell-cell interaction, signal transduction, and embryonicdevelopment. Alternatively spliced transcript variants have beendescribed for this gene. 83_41 13 ATP8A2 intronic ATPase,aminophospholipid transporter Yes ATPase, aminophospholipid transporter,class I, type 8A, member 2 85_23 18 CHST9 intronic carbohydrate(N-acetylgalactosamine cell-cell interaction, signal The protein encodedby this gene belongs to the 4-0) sulfotransferase 9 transduction, andembryonic sulfotransferase 2 family. It is localized to the golgidevelopment, expressed in membrane, and catalyzes the transfer ofsulfate to pituitary position 4 of non-reducing N-acetylgalactosamine(GalNAc) residues in both N-glycans and O- glycans. Sulfate groups oncarbohydrates confer highly specific functions to glycoproteins,glycolipids, and proteoglycans, and are critical for cell-cellinteraction, signal transduction, and embryonic development.Alternatively spliced transcript variants have been described for thisgene. 85_84 3 RP11-735B13.1 processed transcript Homo sapiens 3 BACRP11-735B13 (Roswell Park Cancer Institute Human BAC Library) completesequence. 85_84 3 RP11-735B13.1(5′) processed transcript Homo sapiens 3BAC RP11-735B13 (Roswell Park Cancer Institute Human BAC Library)complete sequence. 85_84 3 RP11-735B13.2(3′) processed transcript 87_2613 NALCN intronic NALCN forms a voltage-independent, Yes NALCN forms avoltage-independent, nonselective, nonselective, noninactivating cationnoninactivating cation channel permeable to Na+, channel permeable toNa+, K+, K+, and Ca(2+). It is responsible for the neuronal and Ca(2+).It is responsible for background sodium leak conductance the neuronalbackground sodium leak conductance 87_26 13 RP11-430M15.1 noveltranscript, antisense to NALCN Yes 87_26 13 RP11-430M15.1 intronic noveltranscript, antisense to NALCN Yes 87_76 8 TRPS1(3′) transcriptionfactor that represses This gene encodes a transcription factor thatGATA-regulated genes and binds to represses GATA-regulated genes andbinds to a a dynein light chain protein dynein light chain protein.Binding of the encoded protein to the dynein light chain protein affectsbinding to GATA consensus sequences and suppresses its transcriptionalactivity. Defects in this gene are a cause of tricho-rhino-phalangealsyndrome (TRPS) types I-III. [provided by RefSeq, July 2008 87_84 1AC093577.1 (5′) * Novel non-coding miRNA genomic clone RELATED to FAM69family of cysteine-rich type II transmembrane proteins. These proteinslocalize to the endoplasmic reticulum but their specific functions areunknown. Alternatively spliced transcript variants encoding multipleisoforms have been observed for this gene. [provided by RefSeq, November2011] 87_84 1 FAM69A 3′-UTR cysteine-rich type II transmembrane Yes Thisgene encodes a member of the FAM69 family endoplasmic reticulum proteinof cysteine-rich type II transmembrane proteins. These proteins localizeto the endoplasmic reticulum but their specific functions are unknown.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. [provided by RefSeq, November 2011]87_84 1 FAM69A intronic cysteine-rich type II transmembrane Yes Thisgene encodes a member of the FAM69 family endoplasmic reticulum proteinof cysteine-rich type II transmembrane proteins. These proteins localizeto the endoplasmic reticulum but their specific functions are unknown.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. [provided by RefSeq, November 2011]87_84 1 FAM69A(5′) cysteine-rich type II transmembrane Yes This geneencodes a member of the FAM69 family endoplasmic reticulum protein ofcysteine-rich type II transmembrane proteins. These proteins localize tothe endoplasmic reticulum but their specific functions are unknown.Alternatively spliced transcript variants encoding multiple isoformshave been observed for this gene. [provided by RefSeq, November 2011]87_84 1 RPL5 intronic ribosomal protein, protein interacts YesRibosomes, the organelles that catalyze protein specifically with thebeta subunit synthesis, consist of a small 40S subunit and a large ofcasein kinase II 60S subunit. Together these subunits are composed of 4RNA species and approximately 80 structurally distinct proteins. Thisgene encodes a ribosomal protein that is a component of the 60S subunit.The protein belongs to the L18P family of ribosomal proteins. It islocated in the cytoplasm. The protein binds 5S rRNA to form a stablecomplex called the 5S ribonucleoprotein particle (RNP), which isnecessary for the transport of nonribosome- associated cytoplasmic 5SrRNA to the nucleolus for assembly into ribosomes. The protein interactsspecifically with the beta subunit of casein kinase II. Variableexpression of this gene in colorectal cancers compared to adjacentnormal tissues has been observed, although no correlation between thelevel of expression and the severity of the disease has been found. Thisgene is co-transcribed with the small nucleolar RNA gene U21, which islocated in its fifth intron. As is typical for genes encoding ribosomalproteins, there are multiple processed pseudogenes of this genedispersed through the genome. [provided by RefSeq, July 2008] 87_84 1RPL5(5′) ribosomal protein, protein interacts Yes Ribosomes, theorganelles that catalyze protein specifically with the beta subunitsynthesis, consist of a small 40S subunit and a large of casein kinaseII 60S subunit. Together these subunits are composed of 4 RNA speciesand approximately 80 structurally distinct proteins. This gene encodes aribosomal protein that is a component of the 60S subunit. The proteinbelongs to the L18P family of ribosomal proteins. It is located in thecytoplasm. The protein binds 5S rRNA to form a stable complex called the5S ribonucleoprotein particle (RNP), which is necessary for thetransport of nonribosome- associated cytoplasmic 5S rRNA to thenucleolus for assembly into ribosomes. The protein interactsspecifically with the beta subunit of casein kinase II. Variableexpression of this gene in colorectal cancers compared to adjacentnormal tissues has been observed, although no correlation between thelevel of expression and the severity of the disease has been found. Thisgene is co-transcribed with the small nucleolar RNA gene U21, which islocated in its fifth intron. As is typical for genes encoding ribosomalproteins, there are multiple processed pseudogenes of this genedispersed through the genome. [provided by RefSeq, July 2008] 87_84 1SNORA66.1 intronic small nucleolar RNA, H/ACA box 66; This gene encodesa non-coding RNA that functions regulation of gene expression in thebiogenesis of other small nuclear RNAs. This RNA is found in thenucleolus, where it may be involved in the pseudouridylation of 18Sribosomal RNA. This RNA is found associated with the GAR1 protein.[provided by RefSeq, April 2009] 87_84 1 U6.1236(5′) * U6 spliceosomalRNA RNA, U6 small nuclear 88_43 10 RP11-428G2.1(5′) * Novel long noncoding RNA 88_64 16 GP2 * intronic glycoprotein 2 Yes glycoprotein 2(zymogen granule membrane) 88_64 16 GP2 * synonymous glycoprotein 2 Yesglycoprotein 2 (zymogen granule membrane) 88_64 16 GP2(3′) *glycoprotein 2 Yes glycoprotein 2 (zymogen granule membrane) 88_8 1AC093577.1 (3′) Novel non-coding miRNA genomic clone RELATED to FAM69family of cysteine-rich type II transmembrane proteins. These proteinslocalize to the endoplasmic reticulum but their specific functions areunknown. Alternatively spliced transcript variants encoding multipleisoforms have been observed for this gene. [provided by RefSeq, November2011] 88_8 1 AC093577.1 (5′) Novel non-coding miRNA genomic cloneRELATED to FAM69 family of cysteine-rich type II transmembrane proteins.These proteins localize to the endoplasmic reticulum but their specificfunctions are unknown. Alternatively spliced transcript variantsencoding multiple isoforms have been observed for this gene. [providedby RefSeq, November 2011] 88_8 1 EVI5 intronic ecotropic viralintegration site 5 Cerebrovascular disease, ecotropic viral integrationsite 5 metabolic syndrome 88_8 1 U6.1077(5′) U6 spliceosomal RNA RNA, U6small nuclear 88_8 6 HACE1(3′) * ubiquitin protein ligase 1 Yes HECTdomain and ankyrin repeat containing E3 ubiquitin protein ligase 1 90_781 AC093577.1 (3′) Novel non-coding miRNA genomic clone RELATED to FAM69family of cysteine-rich type II transmembrane proteins. These proteinslocalize to the endoplasmic reticulum but their specific functions areunknown. Alternatively spliced transcript variants encoding multipleisoforms have been observed for this gene. [provided by RefSeq, November2011] 90_78 1 AC093577.1 (5′) Novel non-coding miRNA genomic cloneRELATED to FAM69 family of cysteine-rich type II transmembrane proteins.These proteins localize to the endoplasmic reticulum but their specificfunctions are unknown. Alternatively spliced transcript variantsencoding multiple isoforms have been observed for this gene. [providedby RefSeq, November 2011] 90_78 1 EVI5 intronic ecotropic viralintegration site 5 Cerebrovascular disease, ecotropic viral integrationsite 5 metabolic syndrome 90_78 1 U6.1077(5′) U6 spliceosomal RNA RNA,U6 small nuclear

For example, as disclosed in Table 2, where a SNP set 9_9 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin NTRK3 and SEMA3A; where a SNP set 10_4 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms in C14orf102,C14orf102(5′), PSMC1, PSMC1(3′), and PSMC1(5′); where a SNP set 12_11 isdisclosed, specifically contemplated herein is that SNP sets detectspolymorphisms in C14orf102, C14orf102(5′), PSMC1, PSMC1(3′), andPSMC1(5′); a SNP set 12_2 is disclosed, specifically contemplated hereinis that SNP sets detects polymorphisms in an intronic region and 3′ UTRof HPGDS, HPGDS(5′), an intronic region, missense, and 3′ UTR ofSMARCAD1 and RP11-363G15.2; where a SNP set 13_12 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin EML5, SPATA7, U4.15(3′), U4.15(5′), and ZC3H14; where a SNP set 14_6is disclosed, specifically contemplated herein is that SNP sets detectspolymorphisms in NTRK3; a SNP set 16_10 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms in, intronicregion and 3′ UTR of HPGDS, HPGDS(5′), RP11-363G15.2 and an intronicregion, missense, and 3′ UTR of SMARCAD1; a SNP set 19_2 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin ARPC5L, an intronic region, missense, and 3′ UTR of GOLGA1, RPL35,WDR38, and SCA1; where a SNP set 21_8 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms inAC068490.2; where a SNP set 22_11 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms inAC068490.2; where a SNP set 25_10 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms inAL158819.7(3′), FOXR2, FOXR2(3′), MAGEH1(5′), PAGE3, PAGE3(3′),PAGE3(5′), RP11-382F24.2, RP11-382F24.2(3′), RP11-382F24.2(5′),RP13-188A5.1, RRAGB, RRAGB(3′), RRAGB(5′), and SNORD112.49(3′); a SNPset 31_2 is disclosed, specifically contemplated herein is that SNP setsdetects polymorphisms in intronic region, and 3′ UTR C6orf138,C6orf138(3′), and OPN5(3′); where a SNP set 41_12 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin GPR119(3′), SLC25A14 and SLC25A14(3′); where a SNP set 42_37 isdisclosed, specifically contemplated herein is that SNP sets detectspolymorphisms in NCAM1, RP11-629G13.1, RP11-629G13.1(3′), AC064837.1,and PPP1R1C; where a SNP set 51_28 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms in IGSF1; aSNP set 52_42 is disclosed, specifically contemplated herein is that SNPsets detects polymorphisms in NCAM1, RP11-629G13.1, andRP11-629G13.1(3′); where a SNP set 54_51 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms in CSMD1;where a SNP set 56_19 is disclosed, specifically contemplated herein isthat SNP sets detects polymorphisms in SNX19(5′); where a SNP set 56_30is disclosed, specifically contemplated herein is that SNP sets detectspolymorphisms in 7SK.207(3′), 7SK.207(5′), PTBP2, PTBP2(5′),RP4-726F1.1(3′), GP2, GP2(3′); where a SNP set 58_29 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin CTD-3025N20.2(3) and RP11-1D12.2(5′); where a SNP set 59_48 isdisclosed, specifically contemplated herein is that SNP sets detectspolymorphisms in RP11-128M1.1, RP11-128M1.1(3′) and TRPS1(3′); where aSNP set 61_39 is disclosed, specifically contemplated herein is that SNPsets detects polymorphisms in IGSF1; where a SNP set 65_25 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin C20orf78(5′); where a SNP set 71_55 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms in NTRK3(3′);where a SNP set 75_31 is disclosed, specifically contemplated herein isthat SNP sets detects polymorphisms in AC093577.1(3′), AC093577.1(5′),U6.1077(5′), and SNX19(5′); where a SNP set 75_67 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin SNORA42.4(5′), VANGL1(5′), RP11-298H24.1(3′), STYK1, AL 161669.1(3′),AL161669.1(5′), AL161669.2, AL161669.2(3′), 5S_rRNA.496(3′), NTRK3(3′),7SK.236(5′), GP2, GP2(3′), CTA-71487.5, RP11-436A20.3, C4orf37,C4orf37(3′), RP11-431J17.1(3′), 7SK.7(3′), DKK4(5′), DUSP4(5′), GSR,RP11-401H2.1 (5′), RP11-486M23.1(5′), RP11-738G5.1(3′), RP11-770E5.1,SLC20A2, SNTG1, SNTGT1(3′), ST18, and VDAC3; where a SNP set 76_63 isdisclosed, specifically contemplated herein is that SNP sets detectspolymorphisms in IGSF1; where a SNP set 76_74 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms inAL161669.1(3′), AL161669.1(5′), AL161669.2, AL161669.2(3′), ABCC12(3′),ITFG1, NETO2, PHKB, PHKB(3′), C4orf37, C4orf37(3′), RP11-431J17.1(3′),SOD3(5′), CTD-2292M14.1(3′), RP11-1D12.2(5′), and RP11-770E5.1; where aSNP set 77_5 is disclosed, specifically contemplated herein is that SNPsets detects polymorphisms in CSMD1; a SNP set 81_13 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin GP2, GP2(3′), RP11-401H2.1(5′), SNTG1, and SNTG1(3′); where a SNP set81_3 is disclosed, specifically contemplated herein is that SNP setsdetects polymorphisms in AC068490.2; where a SNP set 81_73 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin TMEM135, TMEM135(3′), RYR3, and CHST9; where a SNP set 83_41 isdisclosed, specifically contemplated herein is that SNP sets detectspolymorphisms in ATP8A2; where a SNP set 85_84 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin RP11-735B13.1, RP11-735B13.1(5′), and RP11-735B13.2(3′); where a SNPset 85_23 is disclosed, specifically contemplated herein is that SNPsets detects polymorphisms in CHST9; a SNP set 87_26 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin NALCN and RP11-430M15.1; where a SNP set 87_76 is disclosed,specifically contemplated herein is that SNP sets detects polymorphismsin TRPS1(3′); where a SNP set 87_84 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms inAC093577.1(5′), FAM69A, FAM69A(5′), RPL5, RPL5(5′), SNORA66.1, andU6.1236(5′); where a SNP set 88_43 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms inRP11-428G2.1(5′); where a SNP set 88_64 is disclosed, specificallycontemplated herein is that SNP sets detects polymorphisms in GP2 andGP2(3′); where a SNP set 88_8 is disclosed, specifically contemplatedherein is that SNP sets detects polymorphisms in AC093577.1(3′),AC093577.1(5′), EVI5, U6.1077(5′), and HACE1(3′); and where a SNP set90_78 is disclosed, specifically contemplated herein is that SNP setsdetects polymorphisms in AC093577.1(3′), AC093577.1(5′), EVI5, andU6.1077(5′).

It is contemplated herein that the disclosed expression panel cancomprise a single expression set (such as, for example, the SNP setsdisclosed herein 19_2, 88_64, 81_13, 87_76, 58_29, 83_41, 9_9, 10_4,14_6, 56_30, 42_37, 65_25, 71_55, 12_11, 90_78, 77_5, 88_8, 51_28,59_48, 41_12, 22_11, 13_12, 31_22, 85_84, 87_84, 16_10, 56_19, 75_31,81_73, 85_23, 21_8, 76_74, 61_39, 75_67, 76_63, 81_3, 87_26, 88_43,25_10, 12_2, 52_42, or 54_51). It is further contemplated herein thatthe disclosed expression panels can comprise any combination of 2, 3, 4,5, 6, 7, 8, 910, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or42 or more of the disclosed SNP sets. For example, the expression panelcan comprise one or more SNP sets are selected from the group comprising88_8, 90_78, 65_25, 42_37, 71_55, 56_30, 77_5, 12_11, 51_28, 59_48,10_4, 83_41, 58_29, 9_9, 14_6, 87_76, 88_64, or 81_13. Also, theexpression panel can comprise one or more SNP sets are selected from thegroup comprising 10_4, 83_41, 58_29, 9_9, 14_6, 87_76, 88_64, or 81_13.Also, the expression panel can comprise one or more SNP sets areselected from the group comprising 87_76, 88_64, or 81_13.

As disclosed herein, through analysis of the complex genotypic andphenotypic relationships certain groupings of SNP sets andclinical/phenotypic features were elucidated. The composition of thesedesignated sets is presented in Table 7. These SNP sets are associatedwith specific subtypes of the schizophrenias, which are characterizedhere simultaneously by both their genetic features (snp sets) and theirclinical features (phenotypic sets) and are grouped into 8 subtypes(see, Table 7).

TABLE 7 Subset of Genotypic-Phenotypic AND/OR Relationships(Hypergeometric statistics) Phenotypic SNP Schizophrenia Class,Symptoms^(b), and DSM Ratings sets sets p-value Severe process, withpositive and negative symptom schizophrenia (I) Positive symptoms;moderate severity of impairment; unable to function since onset 15_1356_30 2.55E−05 Auditory hallucinations (2 or more voices; runningcommentaries) 12_11 1.79E−04 Auditory hallucinations (2 or more voices;running commentaries); thought echoing; 21_1  3.66E−04 withdrawal;insertion and broadcasting; delusions of mind reading Hallucinations(any); auditory hallucinations (ever; 2 or more voices); grosslydisorganized 50_46 5.70E−04 behavior Hallucinations (mood incongruent);auditory hallucinations; somatic hallucinations 9_6 4.45E−03 (olfactory;gustatory; tactile); religious delusions; delusions of mind reading;delusions of control; thought echoing; withdrawal; insertion andbroadcasting Hallucinations (mood incongruent); persecutory delusions;delusions of reference; jealousy 46_23 4.15E−03 delusions; bizarredelusions; disorganized odd behavior; disorganized odd speech;delusions, fragmented (unrelated themes); delusions, widespread (intrudeinto most aspects of life); thought insertion; flat affect; avolitionand apathy Continuously positive symptoms; severe impairment; continuouscourse; no affective 15_13 75_67 2.31E−13 symptoms Grossly disorganizedbehavior; severe impairment; continuous course 54_11 4.90E−06 Delusionsof persecution and reference; disorganized speech; severe impairment;unable to 30_17 2.56E−04 function since onset Auditory hallucinations(ever; 2 or more voices; running commentaries); jealousy delusions 18_133.50E−04 Thought insertion and withdrawal 27_6  3.62E−03 Hallucinations(any); auditory hallucinations (2 or more voices); grossly disorganized50_46 3.61E−03 behavior Delusions, persecutory and reference; delusionswidespread (intrude into most aspects of 61_18 4.28E−03 life);Disorganized; odd speech 64_11 1.45E−03 Delusions widespread (intrudeinto most aspects of patient's life); continuous course 65_64 1.21E−03Continuously positive symptoms; severe impairment; unable to functionsince onset; no 15_13 76_74 1.07E−07 affective symptoms Delusionswidespread (intrude into most aspects of life) 65_64 1.47E−03 Positiveand negative schizophrenia (II) Auditory hallucinations; delusions(any); bizarre delusions; disorganized speech and 12_4  59_48 1.88E−04behavior; flat affect; alogia; avolition Auditory hallucinations (2 ormore voices; running commentaries); 42_9  71_55 1.98E−03 Negativeschizophrenia (III) Thought insertion and withdrawal 52_28 58_291.44E−04 Disorganized speech; odd speech 7_3 9_9 1.97E−04 Flat affect;persecutory delusions 48_41 2.23E−03 Delusions of mind reading; guiltdelusions; sin delusions; jealousy delusions 26_8 4.20E−03 Flat affect;apathy; avolition 69_41 22_11 5.52E−05 Flat affect; apathy; avolition;alogia; Continuous mixture of positive and negative 10_5  4.62E−04symptoms Disorganized and odd speech 17_2  1.01E−04 Positiveschizophrenia (IV) Hallucinations (any); auditory hallucinations (ever;2 or more voices); no affective 63_24 88_64 3.45E−04 symptoms Delusionsof jealousy; auditory hallucinations (running commentaries) 69_664.49E−03 Severe process, positive schizophrenia (V) Continuouslypositive symptoms; severe impairment; unable to function since onset;22_13 77_5  5.66E−05 no affective symptoms Auditory hallucinations (2+voices; running commentaries)  8_13 3.25E−03 Hallucinations (any);auditory hallucinations (2 or more voices; running 53_6  4.76E−03commentaries); continuous course Auditory hallucinations (ever; voices;noises; music) 59_41 1.22E−03 Continuously positive symptoms; severeimpairment; unable to function since onset; 20_19 81_13 2.83E−04 noaffective symptoms Hallucinations (any); auditory hallucinations (ever;2+ voices); bizarre delusions; 55_7  8.57E−04 delusions fragmented(unrelated themes); delusions widespread (intrude into most aspects oflife) Delusions of reference; Delusions of persecution 34_17 2.40E−03Auditory hallucinations (running commentaries); jealousy delusions 69_661.30E−03 Severe impairment; unable to function since onset; no affectivesymptoms 27_7  25_10 4.76E−06 Auditory hallucinations (2 or more voices;running commentaries) 18_13 9.50E−05 Auditory hallucinations (ever;voices; noises; music); auditory hallucinations (2+ 4_1 2.49E−03 voices;running commentaries); Thought echoing Delusions of reference; delusionsof persecution 66_54 2.10E−03 Bizarre delusions; delusions of mindreading; delusions widespread (intrude into most 8_4 1.93E−03 aspects oflife) Moderate process, disorganized negative (VI) Grossly disorganizedor catatonic behavior; disorganized speech 51_38 19_2  4.03E−04 Moderatedeterioration; unable to function since onset; no affective symptoms42_7  14_6  4.96E−04 Grossly disorganized and inappropriate behavior18_3  2.55E−03 Auditory hallucinations (running commentaries); thoughtechoing 46_29 3.78E−03 Moderate process, positive and negativeschizophrenia (VII) Hallucinations (any); auditory hallucinations (ever;voices; noises; music); continuous 5_2 42_37 1.32E−04 mixture positiveand negative symptoms; continuous course; moderate impairment; unable tofunction since onset; no affective symptoms Bizarre delusions; delusionsof reference 57_39 4.70E−03 Continuous mixture positive and negativesymptoms; continuous course; moderate 11_5  88_43 6.88E−04 impairment;unable to function since onset; no affective symptoms Auditoryhallucinations (ever); bizarre delusions; delusions fragmented(unrelated to 24_4  51_28 9.58E−04 theme) Moderate process, continuouspositive schizophrenia (VIII) No affective symptoms 48_7  16_10 1.44E−03Continuously positive symptoms; severe impairment; unable to functionsince onset; no 28_23 83_41 3.48E−03 affective symptoms Continuouslypositive symptoms; no affective symptoms 25_20 87_26 4.22E−03^(b)Symptoms were assessed with Diagnostic Interview for GeneticStudies.

Because of these associations it is possible to create panels to assessthe risk of a subject to have a particular classification ofschizophrenia. These classification specific expression panels can beused individually in the diagnostic system disclosed herein or as one ofseveral classification specific panels in a diagnostic system. Forexample, in one aspect, disclosed herein are diagnostic systems, whereinthe system selects for severe process, with positive and negativesymptom schizophrenia (I), and wherein the one or more SNP sets comprise56_30, 75_67, or 76_74. Also disclosed are diagnostic systems, whereinthe system selects for positive and negative Schizophrenia (II), andwherein the one or more SNP sets comprise 59_48, 71_55, 21_8, 54_51,31_22, 65_25, or 87_84. Also disclosed are diagnostic systems, whereinthe system selects for negative Schizophrenia (III), and wherein the oneor more SNP sets comprise 58_29, 9_9, 22_11, 81_3, 13_12, 61_39, 10_4,81_73, 75_31, 56_19, 88_8, or 12_2. Also disclosed are diagnosticsystems, wherein the system selects for Positive Schizophrenia (IV), andwherein the one or more SNP sets comprise 88_64, 85_84, or 41_12. Alsodisclosed are diagnostic systems, wherein the system selects for severeprocess, positive schizophrenia (V), and wherein the one or more SNPsets comprise 77_5, 81_13, or 25_10. Also disclosed are diagnosticsystems, wherein the system selects for moderate process, disorganizednegative schizophrenia (VI), and wherein the one or more SNP setscomprise 19_2, 52_42, 90_78, 12_11, 87_76, and 14_6. Also disclosed arediagnostic systems, wherein the system selects for moderate process,positive and negative schizophrenia (VII), and wherein the one or moreSNP sets comprise 42_37, 88_43, or 51_28. Also disclosed are diagnosticsystems, wherein the system selects for moderate process, continuouspositive schizophrenia (VIII), and wherein the one or more SNP setscomprise 16_10, 83_41, or 87_26.

As noted above, the disclosed classification specific expression panelscan be used alone or in combination of 2 or more with any otherclassification specific expression panel. In a non-limiting example, thediagnostic system can comprise classification specific expression panelsI; II; III; IV; V; VI; VII; VIII; I and II; I and III; I and IV; I andV; I and VI; I and VII; I and VIII; II and III; II and IV; II and V; IIand VI; II and VII; II and VIII; III and IV; III and V; III and VI; IIIand VII; III and VIII; IV and V; IV and VI; IV and VII; IV and VIII; Vand VI; V and VII, V and VIII; VI and VII; VI and VIII; VII and VIII; I,II, and III; III and IV; I, II, and V; I, II, and VI; I, II, and VII, I,II, and VIII; I, III, and IV; I, III, and V; I, III, and VI; I, III, andVII; I, III, and VIII; I, IV, and V; I, IV, and VI; I, IV, and VII; I,IV, and VIII; I, V, and VI; I, V, and VII, I, V, and VIII; I, VI, andVII, I, VI, and VIII; I, VII and VIII; I, II, III, and IV; I, II, III,and V; I, II, III, and VI, I, II, III, and VII; I, II, III, and VIII; I,II, IV, and V; I, II, IV, and VI; I, II, IV; and VI; I, II, IV, and VII;I, II, IV, and VIII; I, II, V, and VI; I, II, V, and VII; I, II, V, andVIII; I, II, VI, and VII; I, II, VI, and VIII; I, II, VII, and VIII; I,III, IV, and V; I, III, IV, and VI; I, III, IV, and VII; I, III, IV, andVIII; I, III, V, and VI; I, III, V, and VII; I, III, V, and VIII; I, IV,V, and VI; I, IV, V, and VII; I, IV, V, and VIII; I, V, VI, and VII; I,V, VI, and VIII; I, VI, VII, and VIII; I, II, III, IV, and V; I, II,III, IV, and VI; I, II, III, IV, and VII; I, II, III, IV, and VIII; I,III, IV, V, and VI; I, III, IV, V, and VII; I, III, IV, V, and VIII; I,II, IV, V, and VI; I, II, IV, V, and VII; I, II, IV, V, and VIII; I, II,III, V, and VI; I, II, III, V, and VII; I, II, III, V, and VIII; I, II,III, VI, and VII; I, II, III, VI, and VIII; I, II, III, VII, and VIII;I, II, III, IV, V, and VI; I, II, III, IV, V, and VII; I, I, II, III,IV, V, and VIII; I, I, II, III, IV, VI, and VII; I, II, III, IV, VI, andVIII; I, II, III, IV, VII, and VIII; I, II, III, IV, V, VI, and VII; I,II, III, IV, V, VI, and VIII; I, II, III, IV, V, VI, VII, and VIII; II,III, and IV; II, III, and V; II, III, and VI; II, III, and VII, II, III,and VIII; II, IV, and V; II, IV, and VI; II, IV, and VII; II, IV, andVIII; II, V, and VI; II, V, and VII; II, V, and VIII; II, VI, and VII,II, VI, and VIII; II, VII and VIII; II, III, IV, and V; II, III, IV, andVI; I, II, III, IV; and VI; II, III, IV, and VII; II, III, IV, and VIII;II, IV, V, and VI; II, IV, V, and VII; II, IV, V, and VIII; II, IV, VI,and VII; II, IV, VI, and VIII; II, IV, VII, and VIII; II, III, V, and V;I, II, III, V, and VI; II, III, V, and VII; and II, III, V, and VIII.

In one aspect, it is understood and herein contemplated that expressionpanels can be complemented in the claimed diagnostic system withphenotypic panels which provide the results of clinical assessment,hereditary surveys, environmental surveys (which look at oxidativestress during development or delivery (such as maternal pre-eclampsia ordelivery with low Apgar score), urban versus rural livingconditions—urban life increases risk, use of recreational drugs likemarijuana or PCP during adolescence, social isolation, childhood abuseor neglect, and reduction in sensory input such as hearing or visualloss), online surveys, and interviews creating phenotypic setsAccordingly, in one aspect, disclosed herein are diagnostic systems fordiagnosing schizophrenia further comprising one or more phenotypepanels, wherein each phenotype panel comprises one or more phenotypicsets such as those listed in Table 8. Thus, in one aspect, disclosedherein are diagnostic systems for diagnosing schizophrenia furthercomprising one or more phenotype panels, wherein each phenotype panelcomprises one or more phenotypic sets selected from the group comprising15_13, 12_11, 21_1, 50_46, 9_6, 46_23, 54_11, 30_17, 18_13, 27_6, 61_18,64_11, 65_64, 12_4, 42_9, 52_28, 7_3, 48_41, 26_8, 69_41, 10_5, 17_2,63_24, 69_66, 22_13, 53_6, 59_41, 20_19, 55_7, 34_17, 4_1, 66_54, 8_4,51_38, 42_7, 18_3, 46_29, 5_2, 57_39, 11_5, 24_4, 48_7, 28_23, and/or25_20. It is understood and herein contemplated that the disclosedphenotypic panels can comprise any of the phenotypic sets individuallyor in any combination of 2, 3, 4, 5, 6, 7, 8, 910, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,34, 35, 36, 37, 38, 39, 40, 41, or 42 or more of the disclosed phenotypesets.

As noted in Table 7, the phenotypic sets disclosed herein have beenassociated with one or more symptoms of one or more schizophreniaclasses. Thus, contemplated herein are classification specific phenotypepanels that can be used individually in the diagnostic system disclosedherein or as one of several classification specific panels in adiagnostic system. For example, in one aspect, disclosed herein arediagnostic systems, with positive and negative symptom schizophrenia(I), and wherein the one or more phenotypic sets comprise 15_13, 12_11,21_1, 50_46, 9_6, 46_23, 54_11, 30_17, 18_13, 27_6, 61_18, 64_11, or65_64. Also disclosed are diagnostic systems, wherein the system selectsfor positive and negative schizophrenia (II), and wherein the one ormore phenotypic sets comprise 12_4 or 42_9. Also disclosed arediagnostic systems, wherein the system selects for negativeschizophrenia (III), and wherein the one or more phenotypic setscomprise 52_28, 7_3, 48_41, 26_8, 69_41, 10_5, or 17_2. Also disclosedare diagnostic systems, wherein the system selects for positiveschizophrenia (IV), and wherein the one or more phenotypic sets comprise63_24 and 69_66. Also disclosed are diagnostic systems, wherein thesystem selects for severe process, positive schizophrenia (V), andwherein the one or more phenotypic sets comprise 22_13, 18_13, 53_6,59_41, 20_19, 55_7, 34_17, 69_66, 277, 18_13, 4_1, 66_54, or 8_4. Alsodisclosed are diagnostic systems, wherein the system selects formoderate process, disorganized negative schizophrenia (VI), and whereinthe one or more phenotypic sets comprise 51_38, 427, 18_3, or 46_29.Also disclosed are diagnostic systems, wherein the system selects formoderate process, positive and negative schizophrenia (VII), and whereinthe one or more phenotypic sets comprise 5_2, 57_39, 11_5, or 24_4. Alsodisclosed are diagnostic systems, wherein the system selects formoderate process, continuous positive schizophrenia (VIII), and whereinthe one or more phenotypic sets comprise 48_7, 28_23, or 25_20. As notedabove, the disclosed classification specific phenotype panels can beused alone or in combination of 2 or more with any other classificationspecific phenotype panel in the disclosed diagnostic system.

As noted above, the disclosed classification specific phenotypic panelscan be used alone or in combination of 2 or more with any otherclassification specific phenotype panel. In a non-limiting example, thediagnostic system can comprise classification specific phenotype panelsI; II; III; IV; V; VI; VII; VIII; I and II; I and III; I and IV; I andV; I and VI; I and VII; I and VIII; II and III; II and IV; II and V; IIand VI; II and VII; II and VIII; III and IV; III and V; III and VI; IIIand VII; III and VIII; IV and V; IV and VI; IV and VII; IV and VIII; Vand VI; V and VII, V and VIII; VI and VII; VI and VIII; VII and VIII; I,II, and III; III and IV; I, II, and V; I, II, and VI; I, II, and VII, I,II, and VIII; I, III, and IV; I, III, and V; I, III, and VI; I, III, andVII; I, III, and VIII; I, IV, and V; I, IV, and VI; I, IV, and VII; I,IV, and VIII; I, V, and VI; I, V, and VII, I, V, and VIII; I, VI, andVII, I, VI, and VIII; I, VII and VIII; I, I, II, III, and IV; I, II,III, and V; I, II, III, and VI, I, II, III, and VII; I, II, III, andVIII; I, II, IV, and V; I, II, IV, and VI; I, II, IV; and VI; I, II, IV,and VII; I, II, IV, and VIII; I, II, V, and VI; I, II, V, and VII; I,II, V, and VIII; I, II, VI, and VII; I, II, VI, and VIII; I, II, VII,and VIII; I, III, IV, and V; I, III, IV, and VI; I, III, IV, and VII; I,III, IV, and VIII; I, III, V, and VI; I, III, V, and VII; I, III, V, andVIII; I, IV, V, and VI; I, IV, V, and VII; I, IV, V, and VIII; I, V, VI,and VII; I, V, VI, and VIII; I, VI, VII, and VIII; I, II, III, IV, andV; I, II, III, IV, and VI; I, I, II, III, IV, and VII; I, II, III, IV,and VIII; I, III, IV, V, and VI; I, III, IV, V, and VII; I, III, IV, V,and VIII; I, II, IV, V, and VI; I, II, IV, V, and VII; I, II, IV, V, andVIII; I, II, III, V, and VI; I, II, III, V, and VII; I, II, III, V, andVIII; I, II, III, VI, and VII; I, II, III, VI, and VIII; I, II, III,VII, and VIII; I, I, II, III, IV, V, and VI; I, II, III, IV, V, and VII;I, II, III, IV, V, and VIII; I, I, II, III, IV, VI, and VII; I, II, III,IV, VI, and VIII; I, II, III, IV, VII, and VIII; I, II, III, IV, V, VI,and VII; I, II, III, IV, V, VI, and VIII; I, II, III, IV, V, VI, VII,and VIII; II, III, and IV; II, III, and V; II, III, and VI; II, III, andVII, H, III, and VIII; II, IV, and V; II, IV, and VI; II, IV, and VII;II, IV, and VIII; II, V, and VI; II, V, and VII; II, V, and VIII; II,VI, and VII, II, VI, and VIII; II, VII and VIII; II, III, IV, and V; II,III, IV, and VI; I II, III, IV; and VI; II, III, IV, and VII; II, III,IV, and VIII; II, IV, V, and VI; II, IV, V, and VII; II, IV, V, andVIII; II, IV, VI, and VII; II, IV, VI, and VIII; II, IV, VII, and VIII;II, III, V, and V; II, III, V, and VI; II, III, V, and VII; and II, III,V, and VIII.

It is further understood that a diagnostic system can comprise any oneor combination two or more phenotype panel in combination with any oneor combination of two or more expression panels.

In one aspect, it is disclosed that the diagnostic system can comprise apurpose built analysis and diagnostic system to read the expressionpanel, analyze the expression panel data, input phenotypic sets, anddisplay data and risk profiles associated with having schizophrenia orany particular class of schizophrenia disclosed herein. Thus, in oneaspect, disclosed herein are diagnostic systems of any preceding aspectfurther comprising a means for reading the one or more expressionpanels, a computer operationally linked to the means for reading the oneor more expression panels, and a display for visualizing the diagnosticrisk; wherein the computer identifies the expression profile of anexpression panel, compares the expression profile to a control, andcatalogs that data, wherein the computer provides an input source forinputting phenotypic into a phenomic database; wherein the computercompares the expression and phenomic data and calculates relationshipsbetween the genomic and phenotypic data; wherein the computer comparesthe genomic and phenotypic relationship data to a reference standard;and wherein the computer outputs the relationship data and the standardon the display.

As noted above, the disclosed expression panel can be analyzed or readby any means known in the art including Northern analysis, RNAseprotection assay, PCR, QPCR, genome microarray, DNA microarray,MMCHipslow density PCR array, oligo array, protein array, peptide array,phenotype microarray, SAGE, and/or high throughput sequencing. Thereaders can comprise any of those known in the art including, but notlimited to array readers marked by Affymetrix, Agilent, AppliedMicroarrays, Arrayit, and Illumina.

As disclosed herein protein arrays are solid-phase ligand binding assaysystems using immobilized proteins on surfaces which include glass,membranes, microtiter wells, mass spectrometer plates, and beads orother particles. The assays are highly parallel (multiplexed) and oftenminiaturized (microarrays, protein chips). Their advantages includebeing rapid and automatable, capable of high sensitivity, economical onreagents, and giving an abundance of data for a single experiment.Bioinformatics support is important; the data handling demandssophisticated software and data comparison analysis. However, thesoftware can be adapted from that used for DNA arrays, as can much ofthe hardware and detection systems.

One of the chief formats is the capture array, in which ligand-bindingreagents, which are usually antibodies but can also be alternativeprotein scaffolds, peptides or nucleic acid aptamers, are used to detecttarget molecules in mixtures such as plasma or tissue extracts. Indiagnostics, capture arrays can be used to carry out multipleimmunoassays in parallel, both testing for several analytes inindividual sera for example and testing many serum samplessimultaneously. In proteomics, capture arrays are used to quantitate andcompare the levels of proteins in different samples in health anddisease, i.e. protein expression profiling. Proteins other than specificligand binders are used in the array format for in vitro functionalinteraction screens such as protein-protein, protein-DNA, protein-drug,receptor-ligand, enzyme-substrate, etc. The capture reagents themselvesare selected and screened against many proteins, which can also be donein a multiplex array format against multiple protein targets.

For construction of arrays, sources of proteins include cell-basedexpression systems for recombinant proteins, purification from naturalsources, production in vitro by cell-free translation systems, andsynthetic methods for peptides. Many of these methods can be automatedfor high throughput production. For capture arrays and protein functionanalysis, it is important that proteins should be correctly folded andfunctional; this is not always the case, e.g. where recombinant proteinsare extracted from bacteria under denaturing conditions. Nevertheless,arrays of denatured proteins are useful in screening antibodies forcross-reactivity, identifying autoantibodies and selecting ligandbinding proteins.

Protein arrays have been designed as a miniaturization of familiarimmunoassay methods such as ELISA and dot blotting, often utilizingfluorescent readout, and facilitated by robotics and high throughputdetection systems to enable multiple assays to be carried out inparallel. Commonly used physical supports include glass slides, silicon,microwells, nitrocellulose or PVDF membranes, and magnetic and othermicrobeads. While microdrops of protein delivered onto planar surfacesare the most familiar format, alternative architectures include CDcentrifugation devices based on developments in microfluidics (Gyros,Monmouth Junction, N.J.) and specialised chip designs, such asengineered microchannels in a plate (e.g., The Living Chip™, Biotrove,Woburn, Mass.) and tiny 3D posts on a silicon surface (Zyomyx, HaywardCalif.). Particles in suspension can also be used as the basis ofarrays, providing they are coded for identification; systems includecolour coding for microbeads (Luminex, Austin, Tex.; Bio-RadLaboratories) and semiconductor nanocrystals (e.g., QDots™, Quantum Dot,Hayward, Calif.), and barcoding for beads (UltraPlex™, SmartBeadTechnologies Ltd, Babraham, Cambridge, UK) and multimetal microrods(e.g., Nanobarcodes™ particles, Nanoplex Technologies, Mountain View,Calif.). Beads can also be assembled into planar arrays on semiconductorchips (LEAPS technology, BioArray Solutions, Warren, N.J.).

Immobilization of proteins involves both the coupling reagent and thenature of the surface being coupled to. A good protein array supportsurface is chemically stable before and after the coupling procedures,allows good spot morphology, displays minimal nonspecific binding, doesnot contribute a background in detection systems, and is compatible withdifferent detection systems. The immobilization method used arereproducible, applicable to proteins of different properties (size,hydrophilic, hydrophobic), amenable to high throughput and automation,and compatible with retention of fully functional protein activity.Orientation of the surface-bound protein is recognized as an importantfactor in presenting it to ligand or substrate in an active state; forcapture arrays the most efficient binding results are obtained withorientated capture reagents, which generally require site-specificlabeling of the protein.

Both covalent and noncovalent methods of protein immobilization are usedand have various pros and cons. Passive adsorption to surfaces ismethodologically simple, but allows little quantitative or orientationalcontrol; it may or may not alter the functional properties of theprotein, and reproducibility and efficiency are variable. Covalentcoupling methods provide a stable linkage, can be applied to a range ofproteins and have good reproducibility; however, orientation may bevariable, chemical derivatization may alter the function of the proteinand requires a stable interactive surface. Biological capture methodsutilizing a tag on the protein provide a stable linkage and bind theprotein specifically and in reproducible orientation, but the biologicalreagent must first be immobilized adequately and the array may requirespecial handling and have variable stability.

Several immobilization chemistries and tags have been described forfabrication of protein arrays. Substrates for covalent attachmentinclude glass slides coated with amino- or aldehyde-containing silanereagents. In the Versalinx™ system (Prolinx, Bothell, Wash.) reversiblecovalent coupling is achieved by interaction between the proteinderivatised with phenyldiboronic acid, and salicylhydroxamic acidimmobilized on the support surface. This also has low background bindingand low intrinsic fluorescence and allows the immobilized proteins toretain function. Noncovalent binding of unmodified protein occurs withinporous structures such as HydroGel™ (PerkinElmer, Wellesley, Mass.),based on a 3-dimensional polyacrylamide gel; this substrate is reportedto give a particularly low background on glass microarrays, with a highcapacity and retention of protein function. Widely used biologicalcoupling methods are through biotin/streptavidin or hexahistidine/Niinteractions, having modified the protein appropriately. Biotin may beconjugated to a poly-lysine backbone immobilised on a surface such astitanium dioxide (Zyomyx) or tantalum pentoxide (Zeptosens, Witterswil,Switzerland).

Array fabrication methods include robotic contact printing, ink-jetting,piezoelectric spotting and photolithography. A number of commercialarrayers are available [e.g. Packard Biosciences] as well as manualequipment [V & P Scientific]. Bacterial colonies can be roboticallygridded onto PVDF membranes for induction of protein expression in situ.

At the limit of spot size and density are nanoarrays, with spots on thenanometer spatial scale, enabling thousands of reactions to be performedon a single chip less than 1 mm square. BioForce Laboratories havedeveloped nanoarrays with 1521 protein spots in 85 sq microns,equivalent to 25 million spots per sq cm, at the limit for opticaldetection; their readout methods are fluorescence and atomic forcemicroscopy (AFM).

Fluorescence labeling and detection methods are widely used. The sameinstrumentation as used for reading DNA microarrays is applicable toprotein arrays. For differential display, capture (e.g., antibody)arrays can be probed with fluorescently labeled proteins from twodifferent cell states, in which cell lysates are directly conjugatedwith different fluorophores (e.g. Cy-3, Cy-5) and mixed, such that thecolor acts as a readout for changes in target abundance. Fluorescentreadout sensitivity can be amplified 10-100 fold by tyramide signalamplification (TSA) (PerkinElmer Lifesciences). Planar waveguidetechnology (Zeptosens) enables ultrasensitive fluorescence detection,with the additional advantage of no intervening washing procedures. Highsensitivity can also be achieved with suspension beads and particles,using phycoerythrin as label (Luminex) or the properties ofsemiconductor nanocrystals (Quantum Dot). A number of novel alternativereadouts have been developed, especially in the commercial biotecharena. These include adaptations of surface plasmon resonance (HTSBiosystems, Intrinsic Bioprobes, Tempe, Ariz.), rolling circle DNAamplification (Molecular Staging, New Haven Conn.), mass spectrometry(Intrinsic Bioprobes; Ciphergen, Fremont, Calif.), resonance lightscattering (Genicon Sciences, San Diego, Calif.) and atomic forcemicroscopy [BioForce Laboratories].

Capture arrays form the basis of diagnostic chips and arrays forexpression profiling. They employ high affinity capture reagents, suchas conventional antibodies, single domains, engineered scaffolds,peptides or nucleic acid aptamers, to bind and detect specific targetligands in high throughput manner.

An alternative to an array of capture molecules is one made through‘molecular imprinting’ technology, in which peptides (e.g., from theC-terminal regions of proteins) are used as templates to generatestructurally complementary, sequence-specific cavities in apolymerizable matrix; the cavities can then specifically capture(denatured) proteins that have the appropriate primary amino acidsequence (ProteinPrint™, Aspira Biosystems, Burlingame, Calif.).

Another methodology which can be used diagnostically and in expressionprofiling is the ProteinChip® array (Ciphergen, Fremont, Calif.), inwhich solid phase chromatographic surfaces bind proteins with similarcharacteristics of charge or hydrophobicity from mixtures such as plasmaor tumour extracts, and SELDI-TOF mass spectrometry is used to detectionthe retained proteins.

Large-scale functional chips have been constructed by immobilizing largenumbers of purified proteins and used to assay a wide range ofbiochemical functions, such as protein interactions with other proteins,drug-target interactions, enzyme-substrates, etc. Generally they requirean expression library, cloned into E. coli, yeast or similar from whichthe expressed proteins are then purified, e.g. via a His tag, andimmobilized. Cell free protein transcription/translation is a viablealternative for synthesis of proteins which do not express well inbacterial or other in vivo systems.

For detecting protein-protein interactions, protein arrays can be invitro alternatives to the cell-based yeast two-hybrid system and may beuseful where the latter is deficient, such as interactions involvingsecreted proteins or proteins with disulphide bridges. High-throughputanalysis of biochemical activities on arrays has been described foryeast protein kinases and for various functions (protein-protein andprotein-lipid interactions) of the yeast proteome, where a largeproportion of all yeast open-reading frames was expressed andimmobilised on a microarray. Large-scale ‘proteome chips’ promise to bevery useful in identification of functional interactions, drugscreening, etc. (Proteometrix, Branford, Conn.).

As a two-dimensional display of individual elements, a protein array canbe used to screen phage or ribosome display libraries, in order toselect specific binding partners, including antibodies, syntheticscaffolds, peptides and aptamers. In this way, ‘library against library’screening can be carried out. Screening of drug candidates incombinatorial chemical libraries against an array of protein targetsidentified from genome projects is another application of the approach.

A multiplexed bead assay, such as, for example, the BD™ Cytometric BeadArray, is a series of spectrally discrete particles that can be used tocapture and quantitate soluble analytes. The analyte is then measured bydetection of a fluorescence-based emission and flow cytometric analysis.Multiplexed bead assay generates data that is comparable to ELISA basedassays, but in a “multiplexed” or simultaneous fashion. Concentration ofunknowns is calculated for the cytometric bead array as with anysandwich format assay, i.e. through the use of known standards andplotting unknowns against a standard curve. Further, multiplexed beadassay allows quantification of soluble analytes in samples neverpreviously considered due to sample volume limitations. In addition tothe quantitative data, powerful visual images can be generated revealingunique profiles or signatures that provide the user with additionalinformation at a glance.

C. METHODS

It is understood that use of the disclosed diagnostic system and/orexpression and phenotypic panels can provide the capability to diagnosea subject with schizophrenia, assess the risk of having or developingschizophrenia, classifying a schizophrenia, and targeting a treatment ofa schizophrenia. Accordingly, in one aspect, disclosed herein aremethods of diagnosing a subject with schizophrenia comprising obtaininga biological sample from the subject, obtaining clinical data from thesubject, and applying the biological sample and clinical data to thediagnostic system disclosed herein.

In one aspect, disclosed herein are methods of diagnosing a subject withschizophrenia and/or determining the schizophrenia class comprising:obtaining a biological sample from the subject; obtaining clinical datafrom the subject; applying the biological sample and clinical data to adiagnostic system for diagnosing schizophrenia, wherein the diagnosticsystem comprises one or more expression panels and one or morephenotypic panels; and comparing the genomic and phenotypic panelsresults to a reference standard, for example; wherein the presence ofone or more SNP sets and one or more phenotypic sets in the subjectssample indicates the presence of schizophrenia, and wherein the genomicand phenotypic profile of the reference standard (such as, for exampleTable 7) most closely correlating with the subjects genomic andphenotypic profile indicates schizophrenia class of the subject.

It is understood that any one or combination of the SNP sets disclosedherein can be used in the disclosed methods. Thus, disclosed herein aremethods of diagnosing a subject with schizophrenia and/or determiningthe schizophrenia class, wherein the one or more expression panels eachcomprise one or more of the single nucleotide polymorphism (SNP) setsselected from the group consisting of 19_2, 88_64, 81_13, 87_76, 58_29,83_41, 9_9, 10_4, 14_6, 56_30, 42_37, 65_25, 71_55, 12_11, 90_78, 77_5,88_8, 51_28, 59_48, 41_12, 22_11, 13_12, 31_22, 85_84, 87_84, 16_10,56_19, 75_31, 81_73, 85_23, 21_8, 76_74, 61_39, 75_67, 76_63, 81_3,87_26, 88_43, 25_10, 12_2, 52_42, and 54_51.

Because of these associations noted above in Table 7, it is possible tocreate panels to assess the risk of a subject to have a particularclassification of schizophrenia. These classification specificexpression panels can be used individually in the diagnostic methoddisclosed herein or as one of several classification specific panels ina diagnostic method. For example, in one aspect, disclosed herein arediagnostic methods, wherein the system selects for severe process, withpositive and negative symptom schizophrenia (I), and wherein the one ormore SNP sets comprise 56_30, 75_67, or 76_74. Also disclosed arediagnostic methods, wherein the system selects for positive and negativeSchizophrenia (II), and wherein the one or more SNP sets comprise 59_48,71_55, 21_8, 54_51, 31_22, 65_25, or 87_84. Also disclosed arediagnostic methods, wherein the system selects for negativeSchizophrenia (III), and wherein the one or more SNP sets comprise58_29, 9_9, 22_11, 81_3, 13_12, 61_39, 10_4, 81_73, 75_31, 56_19, 88_8,or 12_2. Also disclosed are diagnostic methods, wherein the systemselects for Positive Schizophrenia (IV), and wherein the one or more SNPsets comprise 88_64, 85_84, or 41_12. Also disclosed are diagnosticmethods, wherein the system selects for severe process, positiveschizophrenia (V), and wherein the one or more SNP sets comprise 77_5,81_13, or 25_10. Also disclosed are diagnostic methods, wherein thesystem selects for moderate process, disorganized negative schizophrenia(VI), and wherein the one or more SNP sets comprise 19_2, 52_42, 90_78,12_11, 87_76, and 14_6. Also disclosed are diagnostic methods, whereinthe system selects for moderate process, positive and negativeschizophrenia (VII), and wherein the one or more SNP sets comprise42_37, 88_43, or 51_28. Also disclosed are diagnostic methods, whereinthe system selects for moderate process, continuous positiveschizophrenia (VIII), and wherein the one or more SNP sets comprise16_10, 83_41, or 87_26. As with the diagnostic systems any combination2, 3, 4, 5, 6, 7, 8, or more of the disclosed expression panels can beused in the diagnostic methods.

It is understood that any one or combination of the phenotype panelsdisclosed herein can be used in the disclosed methods. Thus, disclosedherein are methods of diagnosing a subject with schizophrenia and/ordetermining the schizophrenia class, wherein the one or more phenotypepanels each comprise one or more phenotypic sets selected from the groupconsisting of 15_13, 12_11, 21_1, 50_46, 9_6, 46_23, 54_11, 30_17,18_13, 27_6, 61_18, 64_11, 65_64, 12_4, 42_9, 52_28, 7_3, 48_41, 26_8,69_41, 10_5, 17_2, 63_24, 69_66, 22_13, 53_6, 59_41, 20_19, 55_7, 34_17,27_7, 4_1, 66_54, 8_4, 51_38, 42_7, 18_3, 46_29, 5_2, 57_39, 11_5, 24_4,48_7, 28_23, and 25_20.

As noted in Table 7, the phenotypic sets disclosed herein have beenassociated with one or more symptoms of one or more schizophreniaclasses. Thus, contemplated herein are classification specific phenotypepanels can be used individually in the diagnostic methods disclosedherein or as one of several classification specific panels in adiagnostic method. For example, in one aspect, disclosed herein arediagnostic methods, with positive and negative symptom schizophrenia(I), and wherein the one or more phenotypic sets comprise 15_13, 12_11,21_1, 50_46, 9_6, 46_23, 54_11, 30_17, 18_13, 27_6, 61_18, 64_11, or65_64. Also disclosed are diagnostic methods, wherein the system selectsfor positive and negative schizophrenia (II), and wherein the one ormore phenotypic sets comprise 12_4 or 42_9. Also disclosed arediagnostic methods, wherein the system selects for negativeschizophrenia (III), and wherein the one or more phenotypic setscomprise 52_28, 7_3, 48_41, 26_8, 69_41, 10_5, or 17_2. Also disclosedare diagnostic methods, wherein the system selects for positiveschizophrenia (IV), and wherein the one or more phenotypic sets comprise63_24 and 69_66. Also disclosed are diagnostic methods, wherein thesystem selects for severe process, positive schizophrenia (V), andwherein the one or more phenotypic sets comprise 22_13, 18_13, 53_6,59_41, 20_19, 55_7, 34_17, 69_66, 27_7, 18_13, 4_1, 66_54, or 8_4. Alsodisclosed are diagnostic methods, wherein the system selects formoderate process, disorganized negative schizophrenia (VI), and whereinthe one or more phenotypic sets comprise 51_38, 42_7, 18_3, or 46_29.Also disclosed are diagnostic methods, wherein the system selects formoderate process, positive and negative schizophrenia (VII), and whereinthe one or more phenotypic sets comprise 5_2, 57_39, 11_5, or 24_4. Alsodisclosed are diagnostic methods, wherein the system selects formoderate process, continuous positive schizophrenia (VIII), and whereinthe one or more phenotypic sets comprise 48_7, 28_23, or 25_20. As notedabove, the disclosed classification specific phenotype panels can beused alone or in combination of 2 or more with any other classificationspecific phenotype panel in the disclosed diagnostic methods.

D. EXAMPLES

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how thecompounds, compositions, articles, devices and/or methods claimed hereinare made and evaluated, and are intended to be purely exemplary and arenot intended to limit the disclosure. Efforts have been made to ensureaccuracy with respect to numbers (e.g., amounts, temperature, etc.), butsome errors and deviations should be accounted for. Unless indicatedotherwise, parts are parts by weight, temperature is in .degree. C. oris at ambient temperature, and pressure is at or near atmospheric.

1. Example 1 Uncovering the Hidden Risk Architecture of theSchizophrenias

a) Identifying Many SNP Sets as Candidates for Schizophrenia Risk

We first investigated the genotypic architecture of schizophrenia in theMGS study to identify SNP sets without knowledge of the subject'sclinical status (i.e., case or control). Our exhaustive search uncovered723 nonidentical and possibly overlapping SNP sets in the MGS samples.The SNP sets varied in terms of numbers of both subjects and SNPs. Forexample, one group contains 70 subjects and 24 SNPs, as expected becausefew subjects can share a large number of SNPs. Conversely, another groupcontains 258 subjects and three SNPs, as expected because a large numberof subjects are likely to share only a few SNPs. Initially, we retaineda large number of SNP sets merely to identify the genotypic clusters inall subjects whether they had schizophrenia or not.

b) SNP Sets Vary Greatly in Risk for Schizophrenia

Second, we computed the risk for schizophrenia in carriers of each SNPset (FIG. 3A-F; see also FIG. 4). The risk of schizophrenia was normallydistributed, as expected when capturing the full range of variability.Ninety-eight of the 723 SNP sets had a risk of schizophrenia greaterthan 66% and accounted for 90% of schizophrenia cases in the MGS study.Forty-two SNP sets had a risk of schizophrenia≥70% (Table 1). Forexample, SNP set 192 had a risk of 100%, meaning that all carriers wereschizophrenia cases. The ability of SNP sets to predict schizophreniarisk is illustrated in FIG. 3G. SKAT showed that the association ofschizophrenia with particular SNP sets was stronger than with theaverage effects of their constituent SNPs (Table 1). For example, theSNP set 81_13 has a p value of 1.46E-10, whereas the best and averageSNPs within this set have p values of 2.15E-10 and 5.44E-03,respectively. SKAT and PLINK methods estimated similar p values for theindividual SNPs (R²=0.99; p values for F statistics, <3.83.times.10⁻⁴⁶), showing that SKAT does not inflate results.

The global variance in liability to schizophrenia explained by theaverage effects of all SNPs simultaneously in our sample was 24%. Whileindividual SNPs were mostly low penetrant, many high-risk SNP sets werehighly penetrant (e.g., 100% to 70%; see Table 1) and much moreinformative in predicting schizophrenia risk.

c) Relations Among SNP Sets to One Another and to Gene Products

We show herein that schizophrenia may be an etiologically heterogeneousgroup of illnesses in which some genotypic networks are disjoint, thatis, share neither SNPs nor subjects. To test this, we first checked foroverlap in constituent SNPs and/or subjects among all the SNP sets athigh risk for schizophrenia (see FIG. 8). We found that 17 genotypicnetworks were disjoint, sharing neither SNPs nor subjects (FIG. 5A),suggesting that these have distinct antecedents of schizophrenia. Thesenetworks vary in size and complexity: one highly connected networkassociates 11 SNP sets, whereas eight networks are composed of only asingle isolated SNP set.

We also determined that some SNP sets share SNPs but not subjects (e.g.,59_48 and 87_76; FIG. 5A), as expected because they involve the sameSNPs but with different allele values (both alleles of a SNP can act asrisk alleles in different genetic contexts). In contrast, we found thatthe 58_29 and 41_12 SNP sets do not share SNPs, but independentlyspecify almost the same individuals (FIG. 5A), as expected when, forexample, distinct subsets of genotypic features influence a commondevelopmental pathway. Finally, some SNP sets overlap in both SNPs andsubjects, suggesting that one is a subset within the other (e.g., 88_64and 81_13; see FIG. 4A, 4C). Therefore, the genotypic networks displaydistinct topologies differing in the way constituent SNPs and subjectsare related.

When evaluating whether different genotypic networks operate throughdistinct mechanisms, we found that high-risk SNP sets mapped to variousclasses of genes (e.g., protein coding, ncRNA genes, and pseudogenes)related to known functions and causing different effects on theirproducts (FIG. 4A; see also Tables 2-4 and FIG. 6). We identifieddistinct pathways as exemplified in Table 5. Notably, all of thesepathways are interconnected by the overlapping gene products thatinclude genes previously associated with schizophrenia by GWAS, as wellas genes known to be abnormally expressed in the brains of schizophreniapatients, and other genes not previously identified in prior work (seeTable 6, FIG. 7, and the Pathways section). The emerging picture issuggestive of a possible pathophysiology in which abnormal braindevelopment interacts with environmental events triggering abnormal orexaggerated immune and oxidative processes that increase risk ofschizophrenia.

TABLE 5 Examples of products of genes uncovered by the SNP sets includedin interconnected signaling pathways^(a) Signaling Pathways/ FunctionGenes SNP sets Symptoms Neural development DKK4 75_67 Severe process, +& − STKY1 VANGL1 NCAM1 42_37 Moderate process, + & − 52_42 Moderateprocess, − CHST9 81_73 − EML5 13_12 − SEM3A 9_9 Moderate process, −Neurotrophin function NTRK3 75_67 Severe process, + & − upstream 71_55 +& − region SNTG1 81_13 Severe process, + MAGEH1 25_10 Severe process, +Neurotransmission NETO2, 76_74, 75_67 Severe process, with + & − OPN5 31_22, + NALCN 87_26 Moderate process, continuous + Neuronal functionand SPATA7, 13_12 − neurodegenerative disorders ZC3H14 SLC20A2 41_12 +^(a)The 42 SNP sets at high risk for schizophrenia involved at least 96gene loci, including 54 protein-coding loci and 42 polymorphisms atregulatory sites, as well as 112 polymorphisms in either intergenic orunannotated regions (see full Tables 2 and 6 and FIG. 7)

TABLE 6 Molecular Pathway and Ontologies Identified in theGenotypic-Phenotypic Architecture of SZ (bold, abnormally expressed inthe brains of SZ patients) Gene Name Pathway and Ontology GSR reactiveoxygen species antioxidant/oxidative stress SOD3 reactive oxygen speciesantioxidant/oxidative stress TMEM135 reactive oxygen species/FoxO/DAF-16antioxidant SLC25A14 reactive oxygen species antioxidant/mitochondria/oxidative stress VDAC3 mitochondriaapoptosis/mitochondria/oxidative stress PPP1R1C TNFa;p21/p53/Bcl-2-antagonist/killer, apoptosis/regulation of inhibition ofBcl-2/Bcl-XL intracellular signaling PAGE5 wnt/DKK1 apoptosis WDR38apoptosis RRAGB mTORC1 apoptosis/cell growth/regulation of intracellularsignaling TRPS1 DNA binding/RNF4/dynein apoptosis/gene expression ST18TNFa; interleukin-1alpha/IL-6. apoptosis/gene expression/ neuroimmuneregulation EVI5 GTPase activating protein/Rab11 development, cellmigration/ regulation of intracellular signaling HACE1 Rac1 development,cell migration SCAI integrins; RhoA/Dia1 development, cell migration/transcriptional regulation STYK1 wnt; Akt/GSK-3β development, cellproliferation/cell differentiation CHST9 Golgi sulfatation of proteinsdevelopment, cell/cell interactions ATP8A2 CDC50A related ATPaseneurodevelopment PTCHD4 hedgehog receptor neurodevelopment NCAM1integrins neurodevelopment IGSF1 integrins neurodevelopment SEMA3Aintegrins; neuropilin 1/Plexin A1 neurodevelopment EML5 MAPneurodevelopment DKK4 wnt/bcatenin neurodevelopment GOLGA1 wnt/bcatenin;E-cadherin/Rab11a/b/Arl1 neurodevelopment/protein GTPase synthesis andtrafficking FOXR2 wnt/bcatenin; RAS GTPase/MAPK/ERKneurodevelopment/regulation of intracellular signaling VANGL1 wnt;disheveled 1, 2, 3 neurodevelopment DUSP4 ERK1/2/MAPK; a target of NFkBinhibition neurodevelopment/apoptosis/ regulation of intracellularsignaling CSMD1 Smad3/TGFa/AKT/p53 neurodevelopment/apoptosis/neuroimmune regulation ARPC5L Calmodulin/clathrinneurodevelopment/synaptogenesis NTRK3 MAPK neurotrophins MAGEH1p75/NFkB/cJun/ERK neurotrophins SNTG1 PI2binding/dystrophin/dystobrevin/factor neurotrophins gamma enolase;effector of cathepsin X; effector of TAPP1 NALCN non-voltage dependention channel neuronal excitability RYR3 Calcium/calmodulin neuronalfunction/plasticity/ regulation of intracellular signaling GPR119 Gprotein receptor neurotransmission, cannabioid transmission/neuronalfunction OPN5 NRG1/Erb4 neurotransmission, GABAergictransmission/neuronal function NETO2 GluK2 neurotransmission,glutamatergic transmission/neuronal function SPATA7 consensus sites forPKC/CK-II neurodegenerative disorder/, retinal degeneration ITFG1PP2A/rad3 DNA replication/DNA repair PTBP2 mRNA binding mRNA splicingPRPF31 mRNA binding mRNA splicing RNU4-1 mRNA binding mRNA splicingPSMC1 Ubiquitin protein degradation RPL35 ribosome protein synthesisRPL5 ribosome/casein kinase II protein synthesis/inhibition of cellproliferation/protein synthesis and trafficking SNX19 PI2 binding celltrafficking SMARCAD1 histone H3/H4 deacetylation epigenetic geneexpression SNORA42 ribosome gene expression/protein synthesis andtrafficking SNORD112 ribosome gene expression/protein synthesis andtrafficking NRDE2 siRNA gene expression ABCC12 ATP transport immunityFAM69A immunity in CNS/neuroimmune regulation HPGDS Prostaglandin Dreceptors G protein/NFkB immunity, inflammation, sleep, smoothmuscle/neuroimmune regulation SLC20A2 Sodium/phosphate symporterneurodegenerative disorders/ phosphate metabolism/viral transport PAGE3STPG2 GP2 PHKB Calcium/calmodulin glycogenolysis/regulation ofintracellular signaling

d) Complex Genotypic-Phenotypic Relationships in Schizophrenia

Next we examined whether the complex genetic architecture ofschizophrenia leads to phenotypic heterogeneity. Using data from theDiagnostic Interview for Genetic Studies, as well as from the BestEstimate Diagnosis Code Sheet submitted by GAIN/non-GAIN to dbGaP (seeFIG. 2), we originally identified 342 nonidentical and possiblyoverlapping phenotypic sets of distinct clinical features that clusterin particular cases with schizophrenia (i.e., phenotypic sets orclinical syndromes) without regard for their genetic background.Different SNP sets were significantly associated with particularclinical syndromes (hypergeometric statistics, p values from 2E-13 to1E-03). However, the genotypic-phenotypic relations were complex (i.e.,manyto-many): the same genotypic network could be associated withmultiple clinical outcomes (i.e., multifinality or pleiotropy) anddifferent genotypic networks could lead to the same clinical outcome(i.e., equifinality or heterogeneity; Table 7; see also Table 8). Thegenotypic-phenotypic relations were highly significant by a permutationtest (empirical p value, 4.7E-13; Table 7; see also Table 8).

TABLE 8 Genotypic-Phenotypic AND/OR Relationships.. Hyper- SNP PhenotypeGeometric Sets Sets p-value Phenotype features 22_11 69_41 5.52E−05Avolition_Apathy[I13240] & No_Emotions[I13310] 10_5  4.62E−04No_Emotions[I13310] & Pattern_Sx[I14350] =ContinuousMixtureOfPositiveAndNegative Symptoms & DSM4_Negative_Sx[A60g]& Avolition_Apathy[I13240] & Alogia[I21400] 17_2  1.01E−04Disorganized_Speech[I12990] & Odd_Speech[I13060] &DSM4_Disorganized_Speech[A60e] 25_10 27_7  4.76E−06Severity_Pattern[I14360] = SevereDeterioration &Unable_To_Function_Most_Time_Since_Onset[I21500] &Psychosis_without_Dep_Mania 18_13 9.50E−05 DSM4_2 +Voices_Commented[A60d] & cs_A2a & Aud_2+_Voices[I12170] &Running_Comment[I12100] 4_1 2.49E−03 AH(Voices_Noises_Music)[I12030] &DSM4_2 + Voices_Commented[A60d] & Running_Comment[I12100] &Aud_2+_Voices[I12170] & Thought_Echo[I12240] &Auditory_Halns_Ever[I10920] = Present 66_54 2.10E−03 Del_of_Ref[I11460]& Persecutory_Delusions[I11030] 8_4 1.93E−03DSM4_Definite_Bizarre_Del[A60b] & Delusion_Bizarre[I12020] = Definite &Delusion_Widespread[I12010] = Somewhat & Del_Mind_Reading[I11600] 42_375_2 1.32E−04 Classification_Longitud_SZ[I21560] = Continuous &Unable_To_Function_Most_Time_Since_Onset[I21500] &DSM4_Hallucinations[A60c] & Psychosis_without_Dep_Mania &Auditory_Halns_Ever[I10920] = Present & Severity_Pattern[I14360] =ModerateDeterioration & AH(Voices_Noises_Music)[I12030] &Pattern_Sx[I14350] = ContinuousMixtureOfPositiveAndNegative Symptoms57_39 4.70E−03 cs_A1a & Del_of_Ref[I11460] 51_28 24_4  9.58E−04Delusion_Fragment[I12000] & Delusion_Bizarre[I12020] &Auditory_Halns_Ever[I10920] = Suspected 9_7 1.19E−04 No_Emotions[I13310]& Pattern_Sx[I14350] = ContinuousMixtureOfPositiveAndNegative Symptoms &Psychosis_without_Dep_Mania &Unable_To_Function_Most_Time_Since_Onset[I21500] &Avolition_Apathy[I13240] & DSM4_Negative_Sx[A60g] & Alogia[I21400] 52_241.68E−03 Classification_Longitud_SZ[I21560] = Continuous &Aud_2+_Voices[I12170] & Delusion_Widespread[I12010] = Somewhat 3_22.48E−03 cs_A3 & cs_A1 & cs_A5 & cs_A4 & cs_A2 &Unable_To_Function_Most_Time_Since_Onset[I21500] & cs_A1a &DSM4_Negative_Sx[A60g] 52_42 5_2 1.12E−04Classification_Longitud_SZ[I21560] = Continuous &Unable_To_Function_Most_Time_Since_Onset[I21500] &DSM4_Hallucinations[A60c] & Psychosis_without_Dep_Mania &Severity_Pattern[I14360] = ModerateDeterioration&AH(Voices_Noises_Music)[I12030] & Pattern_Sx[I14350] =ContinuousMixtureOfPositiveAndNegative Symptoms 67_24 1.59E−03No_Emotions[I13310] & DSM4_Negative_Sx[A60g] 54_51 49_36 4.49E−04DSM4_2 + Voices_Commented[A60d] & DSM4_Hallucinations[A60c] &Delusion_Fragment[I12000] = Definite & Auditory_Halns_Ever[I10920] =Present & Running_Comment[I12100] 50_46 1.42E−03DSM4_Gross_Disorganization[A60f] & DSM4_2 + Voices_Commented[A60d] &DSM4_Hallucinations[A60c] 47_40 4.24E−03 Thought_Broadcasting[I11670] &Del_of_Ref[I11460] 56_30 15_13 2.55E−05 Pattern_Sx[I14350] =ContinuouslyPositive & Unable_To_Function_Most_Time_Since_Onset[I21500]& Severity_Pattern[I14360] = SevereDeterioration 12_11 1.79E−04 DSM4_2 +Voices_Commented[A60d] & Running_Comment[I12100] & Aud_2+_Voices[I12170]& cs_A2a & AH(Voices_Noises_Music)[I12030] 21_1  3.66E−04Thought_Echo[I12240] & Thought_Insert[I11740] & Thought_Withdraw[I11810]& Del_Mind_Reading[I11600] & Thought_Broadcasting[I11670] &Running_Comment[I12100] & Aud_2+_Voices[I12170] 50_46 5.70E−04DSM4_Hallucinations[A60c] & DSM4_Gross_Disorganization[A60f] & DSM4_2 +Voices_Commented[A60d] & Auditory_Halns_Ever[I10920] = Present 9_64.45E−03 Thought_Echo[I12240] & Thought_Insert[I11740] &Thought_Withdraw[I11810] & Del_Mind_Reading[I11600] &Thought_Broadcasting[I11670] & Mood_Incongruent_Hal[I17706] &Being_Controlled[I11530] & AH(Voices_Noises_Music)[I12030] &Somatic_Tactile[I12520] & Gustatory_Hal[I12730] & Olfactory_Hal[I12590]& Religious_Delusions[I11320] & Being_Controlled[I11530] 46_23 4.15E−03Persecutory_Delusions[I11030] & Odd_Speech[I13060] &Mood_Incongruent_Hal[I17706] & Delusion_Bizarre[I12020] = Somewhat &Odd_Behavior[I12920] & Delusion_Fragment[I12000] = Somewhat &Del_of_Ref[I11460] & Thought_Insert[I11740] &Delusion_Widespread[I12010] = Somewhat & Jealousy_Delusions[I11110] &Disorganized_Speech[I12990] & No_Emotions[I13310] &Avolition_Apathy[I13240] 59_48 12_4  1.88E−04 cs_A3 & cs_A4 & cs_A1 &cs_A2 & cs_A5 & cs_A1a 75_67 15_13 2.31E−13 Pattern_Sx[I14350] =ContinuouslyPositive & Severity_Pattern[I14360] = SevereDeterioration &Unable_To_Function_Most_Time_Since_Onset[I21500] &Psychosis_without_Dep_Mania 54_11 4.90E−06 Severity_Pattern[I14360] =SevereDeterioration & Classification_Longitud_SZ[I21560] = Continuous &cs_A4 30_17 2.56E−04 Persecutory_Delusions[I11030] &Unable_To_Function_Most_Time_Since_Onset[I21500] &Severity_Pattern[I14360] = SevereDeterioration & Odd_Speech[I13060] &Del_of_Ref[I11460] 18_13 3.50E−04 DSM4_2 + Voices_Commented[A60d] &Running_Comment[I12100] & cs_A2a & Aud_2+_Voices[I12170] &AH(Voices_Noises_Music)[I12030] & Auditory_Halns_Ever[I10920] = Present& Jealousy_Delusions[I11110] 27_6  3.62E−03 Thought_Insert[I11740] &Thought_Withdraw[I11810] 50_46 3.61E−03 DSM4_Gross_Disorganization[A60f]& DSM4_2 + Voices_Commented[A60d] & DSM4_Hallucinations[A60c] 61_184.28E−03 Persecutory_Delusions[I11030] & Delusion_Widespread[I12010] =Somewhat & Del_of_Ref[I11460] 64_11 1.45E−03 cs_A3 & Odd_Speech[I13060]65_64 1.21E−03 Delusion_Widespread[I12010] = Somewhat &Classification_Longitud_SZ[I21560] = Continuous 76_74 15_13 1.07E−07Severity_Pattern[I14360] = SevereDeterioration & Pattern_Sx[I14350] =ContinuouslyPositive & Unable_To_Function_Most_Time_Since_Onset[I21500]& Psychosis_without_Dep_Mania 65_64 1.47E−03 Delusion_Widespread[I12010]= Somewhat & Classification_Longitud_SZ[I21560] = Continuous & cs_A477_5  22_13 5.66E−05 Severity_Pattern[I14360] = SevereDeterioration &Psychosis_without_Dep_Mania &Unable_To_Function_Most_Time_Since_Onset[I21500] & Pattern_Sx[I14350] =ContinuouslyPositive 18_13 3.25E−03 DSM4_2 + Voices_Commented[A60d] &cs_A2a & Aud_2+_Voices[I12170] & Running_Comment[I12100] 53_6  4.76E−03Classification_Longitud_SZ[I21560] = Continuous &DSM4_Hallucinations[A60c] & DSM4_2 + Voices_Commented[A60d] & cs_A2a &59_41 1.22E−03 AH(Voices_Noises_Music)[I12030] &Auditory_Halns_Ever[I10920] = Present 81_13 20_19 2.83E−04Pattern_Sx[I14350] = ContinuouslyPositive & Severity_Pattern[I14360] =SevereDeterioration & Unable_To_Function_Most_Time_Since_Onset[I21500] &Psychosis_without_Dep_Mania 55_7  8.57E−04 DSM4_2 +Voices_Commented[A60d] & DSM4_Hallucinations[A60c] &Delusion_Fragment[I12000] = Somewhat & Delusion_Widespread[I12010] =Somewhat & Delusion_Bizarre[I12020] = Somewhat &Delusion_Fragment[I12000] = Definite & Auditory_Halns_Ever[I10920] =Present 34_17 2.40E−03 Del_of_Ref[I11460] &Persecutory_Delusions[I11030] 69_66 1.30E−03 Jealousy_Delusions[I11110]& cs_A2a 90_78 22_7  7.29E−04 Pattern_Sx[I14350] =ContinuousMixtureOfPositiveAndNegative Symptoms & No_Emotions[I13310] &Unable_To_Function_Most_Time_Since_Onset[I21500] 65_55 4.51E−04Guilt_Sin_Delusions[I11180] & Persecutory_Delusions[I11030] & cs_A4 &Del_of_Ref[I11460] 70_43 4.37E−03 DSM4_Gross_Disorganization[A60f] &Odd_Behavior[I12920] & Avolition_Apathy[I13240] 10_4  66_50 2.45E−04Unable_To_Function_Most_Time_Since_Onset[I21500] &Classification_Longitud_SZ[I21560] = Continuous 43_20 3.14E−04Thought_Insert[I11740] & Thought_Withdraw[I11810] 64_37 3.32E−03 cs_A3 &cs_A4 12_11 29_13 4.30E−04 Severity_Pattern[I14360] =SevereDeterioration & Pattern_Sx[I14350] =ContinuousMixtureOfPositiveAndNegative Symptoms &Delusion_Widespread[I12010] = Definite & Psychosis_without_Dep_Mania33_13 1.92E−03 Guilt_Sin_Delusions[I11180]] & Delusion_Bizarre[I12020]12_2  67_24 4.83E−03 DSM4_Negative_Sx[A60g] & No_Emotions[I13310] 30_294.36E−03 Del_of_Ref[I11460] & Somatic_Tactile[I12520] 13_12 27_206.26E−04 Psychosis_without_Dep_Mania[A620] & Disorganized_Speech[I12990]& DSM4_Disorganized_Speech[A60e] 27_22 1.38E−03Thought_Broadcasting[I11670] & Del_Mind_Reading[I11600] & cs_A1a 58_161.56E−03 DSM4_Negative_Sx[A60g] & Persecutory_Delusions[I11030] &Avolition_Apathy[I13240] 14_6  42_7  4.96E−04Unable_To_Function_Most_Time_Since_Onset[I21500] &Severity_Pattern[I14360] = ModerateDeterioration &Severity_Pattern[I14360] = ModerateDeterioration &Psychosis_without_Dep_Mania 18_3  2.55E−03 Disorg/Inapp_Behav[I21050] &DSM4_Gross_Disorganization[A60f] 46_29 3.78E−03 Thought_Echo[I12240] &cs_A2a 16_10 48_7  1.44E−03 Psychosis_without_Dep_Mania 21_8  13_111.56E−04 DSM4_2 + Voices_Commented[A60d] & Aud_2+_Voices[I12170] &Running_Comment[I12100] & cs_A2a & AH(Voices_Noises_Music)[I12030] 64_464.19E−04 Alogia[I21400] & No_Emotions[I13310] & Avolition_Apathy[I13240]62_35 2.89E−03 Del_of_Ref[I11460] & Being_Controlled[I11530] 31_22 24_8 2.93E−03 Delusion_Fragment[I12000] = Definite &DSM4_Definite_Bizarre_Del[A60b] & Delusion_Bizarre[I12020] = Definite &Delusion_Widespread[I12010] = Somewhat 62_26 1.88E−03Thought_Insert[I11740] & Aud_2+_Voices[I12170] & Running_Comment[I12100]41_12 58_28 6.04E−04 Return_Normal_for_2Months[I13600] &Severity_Pattern[I14360] = MildDeterioration 23_16 2.50E−03Severity_Pattern[I14360] = MildDeterioration &Classification_Longitud_SZ[I21560] = EpisodicWithInterepisodeResidualSymptoms & Delusion_Widespread[I12010] = Definite &Auditory_Halns_Ever[I10920] & Classification_Longitud_SZ[I21560] =SingleEpisodeInPartial Remission & Pattern_Sx[I14350] =PredominantlyPositiveConvertingToPre dominantlyNegative &Return_Normal_for_2Months[I13600] 56_19 33_13 4.30E−04Guilt_Sin_Delusions[I11180] & Psychosis_without_Dep_Mania 58_29 52_281.44E−04 Thought_Insert[I11740] & Thought_Withdraw[I11810] 61_39 64_485.11E−05 Delusion_Widespread[I12010] = Somewhat &Classification_Longitud_SZ[I21560] = Continuous 32_9  2.79E−03Thought_Insert[I11740] & Thought_Withdraw[I11810] 65_25 36_14 5.53E−04Thought_Broadcasting[I11670] & Del_Mind_Reading[I11600] & cs_A1a 31_293.76E−04 cs_A3 & cs_A4 & cs_A5 & cs_A2 & cs_A1 & cs_A1a 61_21 5.55E−03Del_Mind_Reading[I11600] & Thought_Broadcasting[I11670] &Thought_Insert[I11740] & Psychosis_without_Dep_Mania[A620] 75_31 44_3 6.37E−04 cs_A4 & Unable_To_Function_Most_Time_Since_Onset[I21500] &cs_A3 64_6  1.55E−03 DSM4_Disorganized_Speech[A60e] &Disorganized_Speech[I12990] & Pattern_Sx[I14350] =ContinuousMixtureOfPositiveAndNegative Symptoms 81_3  34_33 1.96E−03Psychosis_without_Dep_Mania & Delusion_Fragment[I12000] = Somewhat 46_254.51E−03 Avolition_Apathy[I13240] & No_Emotions[I13310] & DSM4_2 +Voices_Commented[A60d] 81_73 19_12 2.46E−04 Disorg/Inapp_Behav[I21050] &DSM4_Gross_Disorganization[A60f] 59_12 2.20E−04 Odd_Behavior[I12920] &Disorg/Inapp_Behav[I21050] 85_84 38_2  6.10E−04 Delusion_Bizarre[I12020]= Definite & DSM4_Definite_Bizarre_Del[A60b] & Delusion_Fragment[I12000]= Definite 49_36 3.28E−03 DSM4_2 + Voices_Commented[A60d] &DSM4_Hallucinations[A60c] & Delusion_Fragment[I12000] = Definite &Auditory_Halns_Ever[I10920] = Present 58_4  4.81E−03Auditory_Halns_Ever[I10920] = Present & DSM4_Hallucinations[A60c] &cs_A2 87_26 25_20 4.22E−03 Pattern_Sx[I14350] = ContinuouslyPositive &Psychosis_without_Dep_Mania 87_76 14_10 5.12E−04 Pattern_Sx[I14350] =ContinuousMixtureOfPositiveAndNegative Symptoms &Unable_To_Function_Most_Time_Since_Onset[I21500] 64_6  2.19E−04DSM4_Disorganized_Speech[A60e] & Disorganized_Speech[I12990] & cs_A462_60 1.83E−03 Avolition_Apathy[I13240] &Classification_Longitud_SZ[I21560] = Continuous 59_13 4.12E−03No_Emotions[I13310] & Classification_Longitud_SZ[I21560] = Continuous &Pattern_Sx[I14350] = ContinuousMixtureOfPositiveAndNegative Symptoms &DSM4_Negative_Sx[A60g] 88_43 11_5  6.88E−04 Pattern_Sx[I14350] =ContinuousMixtureOfPositiveAndNegative Symptoms &Unable_To_Function_Most_Time_Since_Onset[I21500] &Psychosis_without_Dep_Mania & Severity_Pattern[I14360] =ModerateDeterioration 16_1  7.77E−04 Delusion_Fragment[I12000] &Delusion_Bizarre[I12020] 52_8  1.68E−03 Disorg/Inapp_Behav[I21050] &cs_A4 & DSM4_Gross_Disorganization[A60f] 18_17 2.90E−03Del_Mind_Reading[I11600] & Thought_Broadcasting[I11670] &Thought_Insert[I11740] 66_12 2.25E−03 AH(Voices_Noises_Music)[I12030] &Auditory_Halns_Ever[I10920] = Present & DSM4_Hallucinations[A60c] 88_6463_24 3.45E−04 DSM4_2 + Voices_Commented[A60d] &DSM4_Hallucinations[A60c] & Auditory_Halnss_Ever[I10920] = Present &Psychosis_without_Dep_Mania[A620] 69_66 4.49E−03Jealousy_Delusions[I11110] & cs_A2a 88_8  13_4  4.49E−03DSM4_Disorganized_Speech[A60e] & Disorganized_Speech[I12990] &Odd_Speech[I13060] 9_9 7_3 1.97E−04 DSM4_Disorganized_Speech[A60e] &Odd_Speech[I13060] & Disorganized_Speech[I12990] 48_41 2.23E−03No_Emotions[I13310] & Persecutory_Delusions[I11030] 26_8  4.20E−03Jealousy_Delusions[I11110] & Guilt_Sin_Delusions[I11180] &Del_Mind_Reading[I11600] 19_2  51_38 4.03E−04 cs_A4 & cs_A3 71_55 42_9 1.98E−03 Running_Comment[I12100] & DSM4_2 + Voices_Commented[A60d] 83_4128_23 3.48E−03 Pattern_Sx[I14350] = ContinuouslyPositive &Severity_Pattern[I14360] = SevereDeterioration &Unable_To_Function_Most_Time_Since_Onset[I21500] &Psychosis_without_Dep_Mania 87_84 68_19 8.19E−04 cs_A1a &Del_of_Ref[I11460]

Specifically, we identified a phenotypic set indicating a generalprocess of severe deterioration (i.e., continuous positive symptoms withmarked and progressive impairment) that was associated with many SNPsets (e.g., SNP sets 75_67 and 56_30, with p values, 2.3E-13 and2.55E-05, respectively; Table 7, FIG. 5A). Other SNP sets wereassociated with a general process of moderate deterioration (moderate orfluctuating impairment despite a continuous mixture of symptoms), as inSNP sets 14_6, and 42_37 (p values, 5F-04; Table 7, FIG. 5A). Weidentified specific clinical syndromes that were unambiguouslyassociated with particular genotypic networks. For example, specificphenotypic sets differentiate among SNP sets even within the samenetwork, which illustrate similar but not identical forms ofmultifinality in schizophrenia (e.g., 76_74 and 58_29; Table 7, FIG. 5A,blue lines). Particular phenotype sets can also distinguish SNP setsconnected only by shared subjects (FIG. 5A, red lines). For example, SNPset 76_74 shares subjects with 56_30 and with 81_13; however, the latterSNP sets are associated with a specific phenotypic set not present in76_74 (Table 7).

e) Positive and Negative Symptoms Differentiate Classes of Schizophrenia

Genotypic and phenotypic relationships could be grouped into eightclasses of schizophrenia, as shown in FIG. 3B and Table 3. First, weidentified SNP sets involving subjects with predominantly positivesymptoms (e.g., 41_12 and 88_64) and few residual symptoms. Second, weidentified SNP sets represented by predominantly negative anddisorganized symptoms (e.g., 10_4 and 61_39), decreased psychosocialfunction, and continuous residual symptoms. Bizarre delusions andsymptoms of cognitive and behavioral disorganization, such as thoughtinsertion and disorganized speech among others, were accepted as fuzzyindicators of either positive or negative classes of schizophrenia butwere considered to be more common in negative and disorganized classes(e.g., in Table 7, thought echo and commenting hallucinations in“negative schizophrenia” with phenotypic set 46_29 associated with SNPset 14_6). Third, several SNP sets harbor mixed positive and negativesymptoms (e.g., 59_48 and 54_51). These three classes were enriched byconsidering the general severe and moderate patterns, which werefrequent in several networks (FIG. 5B), as described above. Because thelatter patterns appear in combination with a set of only positivesymptoms (e.g., 81_13), both positive and negative symptoms (e.g.,75_67), and only negative symptoms (e.g., 19_2), we were able toclassify schizophrenia into eight classes (FIG. 5B).

f) Replication of Results in Two Independent Samples

We tested the replicability of our findings in the MGS study by carryingout the same analyses of the genotypic and phenotypic architecture ofschizophrenia in the CATIE and Portuguese Island samples. A total of1,303 SNPs were shared between the selected SNPs in the MGS and CATIEsamples, and 1,234 SNPs between the MGS and Portuguese Island samples.Imputed variants were not considered, to avoid possible biases.

Together, both samples reproduced at least 81% of the SNP sets at risk(see Table 9). In addition, most of the SNP sets replicated in the twoPGC samples achieved risk values as high as those of the MGS sample(>70%: 70% of those identified exhibit >70% risk, and 90% show >60%risk. Some SNP sets exhibited slightly higher risk values than those inthe MGS sample. The genotypic-phenotypic relations in CATIE and thePortuguese Island studies closely matched those observed in the MGSstudy (hypergeometric statistics, p values 2E-13 to 1E-03). The eightschizophrenia classes exhibited high reproducibility. For example,except for one relation (“−” in the MGS study and “+ and −” in CATIE;see Table 9), all relations exhibited similar positive and negativesymptoms in the MGS study and CATIE. Three relations showed lessspecific symptoms in CATIE than in the MGS study, as expected becauseCATIE did not use the Diagnostic Interview for Genetic Studies.

TABLE 9 Summary of the Reproducibility of the Molecular Genetics ofSchizophrenia Dataset in the CATIE and the Portuguese Islands StudiesGain/nonGain CATIE Portuguese SNP SNP Symptom SNP Symptom sets RiskSymptoms sets Risk Variation* sets Risk Variation*  9_9 0.92 −  9_9  5_10.97 40_40 0.67 19_2 1.00 moderate − 19_2 25_7 1.00 26_3 0.88 21_8 0.71+− 21_8 25_19 0.61 general +− 10_2 0.88 81_13 0.95 severe + 81_13 12_30.60 22_11 0.75 − 22_11 16_10 0.71 general − 15_9 0.71 25_10 0.70severe + 25_10 33_28 0.70 general +− 10_4 0.91 − 10_4 13_2 0.64 35_110.86 59_48 0.80 +− 36_18 0.68 severe +− 12_11 0.84 moderate − 12_11 14_90.70 35_11 0.86 56_30 0.88 severe +− 56_30 32_10 0.60 35_31 0.83severe/moderate +− 12_2 0.70 − 12_2 37_11 0.84 14_5 0.88 13_12 0.75 −13_12 11_8 0.80 29_13 0.70 14_6 0.90 moderate − 14_6 12_12 0.60 40_400.67 16_10 0.73 general − 16_10 14_3 1.00 14_5 0.88 31_22 0.74 +− 31_2225_16 0.71 19_5 0.76 41_12 0.76 + 42_37 0.86 moderate +− 42_37 19_140.92 25_21 0.74 51_28 0.81 moderate +− 76_74 0.71 severe +− 76_74 33_111.00 40_37 0.78 moderate 52_42 0.70 moderate − 52_42 40_18 0.60 − 25_210.74 +− 54_51 0.70 +− 36_1 0.55 no match 56_19 0.73 − 58_29 0.94 − 58_2931_6 1.00 32_6 0.65 +− 61_39 0.71 − 65_25 0.86 +− 90_78 0.83 moderate −90_78  4_2 0.93  3_1 0.62 71_55 0.86 +− 71_55 35_11 0.65 27_22 0.7375_31 0.73 − 75_31 39_30 1.00  3_1 0.62 75_67 0.71 severe +− 75_67  8_30.70 23_5 0.76 76_63 0.71 general/mild 88_64 0.96 + 88_64 35_2 0.61 77_50.82 severe + 36_1 0.55 no match 81_3 0.71 − 81_3 16_10 0.71 10_2 0.88−+ 81_73 0.73 − 81_73 36_12 0.74 27_23 0.73 general − 83_41 0.93general/mild 83_41 39_3 0.60 85_23 0.73 general/mild 85_84 0.74 + 87_260.71 general/mild 87_26 38_30 0.50 38_7 0.75 general +− 87_76 0.95moderate − 87_76  3_3 0.50 34_22 0.68 87_84 0.74 +− 87_84  9_4 0.50 40_91.00 88_43 0.71 moderate +− 88_43 30_21 0.50 15_11 0.74 88_8 0.82 − 88_839_30 1.00 39_31 0.56 +− (*empty values indicates similar results tothose corresponding to Gain/nonGain)

We found few differences when comparing the MGS and Portuguese Islandstudies (see Table 9), except differences in severity that preserved thesign of the symptoms. Three relations with negative symptoms in the MGSstudy exhibited negative and positive symptoms in the Portuguese Islandsample (see Table 9). Only two SNP sets in the Portuguese Island samplehad no significant crossmatch with the phenotypic features expected fromthe MGS study.

2. Example 2

We first identified sets of interacting single-nucleotide polymorphisms(SNPs) that cluster within subgroups of individuals (SNP sets)regardless of clinical status in the MGS Consortium study, employing ourgeneralized factorization method combined with non-negative matrixfactorization to identify candidates for functional clusters (see FIG.2). This approach performs an unsupervised co-clustering of subjectstogether with distinguishing genotypic/phenotypic features based on theempirical data alone. We combined the Genetic Association InformationNetwork (GAIN) and non-GAIN samples of the MGS study, which constituteone GWAS. The 4,196 cases and 3,827 controls in the MGS study werecombined to identify SNP sets. We had data of good quality on 696,788SNPs on these cases and controls, and from these we preselected 2,891SNPs that had at least a loose association (p values<1.0.times.10⁻²)with a global phenotype of schizophrenia. SNP sets were labeled by apair of numbers based on the order in which they were chosen by thealgorithm. Each SNP set was composed of a particular group of subjectsdescribed by a particular set of homozygotic and/or heterozygoticalleles; subjects and/or SNPs may be present in more than one set. TheSNP sets identified by our generalized factorization method are optimalclusters of SNPs in particular subjects that encode AND/OR interactionsbetween SNPs and subjects (FIG. 3A-F, Table 1; see also FIG. 4). TheseSNP sets and their relations with one another characterize the geneticarchitecture of schizophrenia-associated SNPs in all subjects, includingcases and controls (FIG. 1A).

Second, we examined the risk of schizophrenia for each SNP set andidentified those with high risk. The statistical significance of theassociation of SNP sets with schizophrenia was calculated using theSNP-Set Kernel Association Test (SKAT) program, which properly accountsfor multiple comparisons.

Third, we checked for significant overlap among SNP sets in terms ofsubjects and/or SNPs using hypergeometric statistics (see FIG. 2). Thisallowed us to characterize the relations among SNP sets and to identifySNP sets that were connected to each other by having certain SNPs orsubjects in common, thereby composing genotypic networks. Disjointnetworks shared neither SNPs nor subjects, as expected if schizophreniais a heterogeneous group of diseases.

Fourth, we identified sets of distinct clinical features that cluster inparticular cases with schizophrenia (i.e., phenotypic sets or clinicalsyndromes) without regard for their genetic background, again usingnon-negative matrix factorization. Ninety-three clinical features ofschizophrenia from interviews based on the Diagnostic Interview forGenetic Studies, as well as the Best Estimate Diagnosis Code Sheetsubmitted by GAIN/non-GAIN to dbGaP, were initially considered with theMGS sample. The Diagnostic Interview for Genetic Studies was utilizedfor the Portuguese Island samples. Corresponding features were extractedin CATIE kern the Positive and Negative Syndrome Scale, the Quality ofLife Questionnaire, and the Structured Clinical Interview for DSM-IV.These phenotypic sets and their relations with one another characterizethe phenotypic architecture of schizophrenia (FIG. 1B).

Fifth, we tested whether SNP sets were associated with distinctphenotypic sets in the MGS sample, and we tested the replicability ofthese relations in the two other independent studies. Replication wasevaluated in terms of replication of the SNP sets and theircorresponding risk, as well as the relationships between SNP sets andphenotypic sets. In the samples that used the Diagnostic Interview forGenetic Studies (the MGS and Portuguese Island samples), the specificphenotypic features can be compared. Since the CATIE study did not usethe Diagnostic Interview for Genetic Studies, we estimated thecorresponding symptoms from available phenotypic data (based on thePositive and Negative Syndrome Scale, the Quality of Life Questionnaire,and the Structured Clinical Interview for DSM-IV). Genotypic andphenotypic data were available for 738 cases in CATIE and 346 cases inthe Portuguese Island study. The significance of cohesive relationsamong SNP sets and clinical syndromes was tested using hypergeometricstatistics. The relations between the genotypic and phenotypic clusterscharacterize the genotypic-phenotypic architecture (FIG. 1C).

a) Genomics Dataset: Gain and NonGain Studies

We first investigated the architecture of schizophrenia (SZ) using theGain and NonGain genome wide association studies (GWAS) as our maintargets, which are coherent case-control studies performed in a singlelab under similar conditions. This study contains data from 8023subjects, 4196 patients and 3827 controls, combining data fromEuro-American ancestry (EA) and African-American ancestry (AA).Genotyping was carried using the Affymetrix 6.0 array, which assays906,600 SNPs.

This study was originally performed in part at Washington University.Study population, ascertainment, phenomics and genomic datasets, as wellas other information relative to this study can be accessed in the dbGaPby their identifiers: phs000021.v3.p2 and phs000167.vl.p1 for GAIN andNonGAIN projects, respectively.

The genotype data was codified in a matrix [SNPs.times.subjects], wherethe columns and rows correspond to subjects and SNPs, respectively. Ineach cell of the matrix, the value for the corresponding SNP and subjectis assigned as 1, 2, and 3 for the SNP allele values AA, AB, and BB,respectively. Missing values were initialized by 0.

b) Data Cleaning

The quality control (QC) of the genotypic data was performed followingthe steps removing consequently all the SNPs satisfying the nextcriteria:

1) SNP call rate <95% in either GAIN or NonGAIN or combined datasets.

2) Hardy-Weinberg (HWE) p-value <10E-06 based on control samples ineither GAIN or NonGAIN or combined, (using only females for chr X SNPs).

3) Minor Allele Frequency (MAF) <1% in combined dataset.

4) Failed plate effect test in GAIN, NonGAIN or combined dataset.

5) MENDEL errors>2 in either GAIN or NonGAIN.

6) >1 disconcordant genotypes in either GAIN 29 duplicates or NonGAIN 32duplicates.

7) >2 disconcordant genotypes for 93 (=3.times.31 trios) samplesgenotyped in both GAIN and NonGAIN.

A total of 209,321 SNPs were excluded due to the restrictions describedabove from the total 906,109 SNPs genotyped. Therefore, 696,788 SNPspassed the QC filters. Then, 2891 SNPs were pre-selected to reduce thelarge search space using the logistic association function included inthe PLINK software suite, taking sex and ancestry as co-variates, andestablishing a generous threshold (p-value <0.01). This threshold wasestablished as 0.01 because this is approximately the value used in thesupplementary tables reported in previously for AA, EA and AA-EAanalyses.

c) Methodology: A Divide & Conquer Strategy to Dissect a GWAS into theGenotypic-Phenotypic Architecture of a Disease

To uncover the architecture of SZ we applied a “Divide & Conquer”strategy (see FIG. 2) that is commonly used in computer science to solvecomplex problems such as those of proteomics and transcriptomics andcancer identification. Here we applied this strategy to dissect a singleGWAS into multiple genotypic and/or phenotypic networks, as an attemptto extract the maximum information even from one dataset.

The “divide” step deconstructs genotypic and phenotypic dataindependently, and explores multiple local patterns (i.e., SNP sets andphenotypic sets). We used non-negative matrix factorization methods thathave been applied to characterize complex genomic and social profiles,and generalized them to approach GWA data in a purely data-driven andunbiased fashion.

Thus, our systematic grouping strategy is not directed by previousknowledge of polygenic involvement in SZ, does not limit subjects toonly one SNP set, and does not predefine the number of SNP sets,avoiding possible biases and 4 assumptions that relationships arelinear, regular, or random. Unlike other approaches, we do not constrainSNP sets to a particular genome feature or to be in linkagedisequilibrium (LD), and the phenotypic status of the subjects is notconsidered in SNP set formation (i.e., it is unsupervised).

After incorporating phenotypic status a posteriori within each set(e.g., cases and controls), we establish their statistical significancewith powerful and well-founded test methods that perform the appropriatecorrections for the use of SNP sets, as well as provide an unbiased risksurface of disease to test predictions.

The “conquer” step consists of three stages. First, assembling theuncovered local components of the genotypic architecture into genotypicnetworks of SNP sets, where two SNP sets are connected if they (i)comprise different sets of subjects described by similar sets of SNPs,(ii) and/or if they have similar sets of subjects but characterized bydistinct sets of SNPs, (iii) and/or if one of the two SNP sets containsa subset of subjects and SNPs of the other SNP set. Second, optimallycombining the local components of the phenotypic architecture (i.e.,phenotypic sets) with the genotypic sets to expose the jointgenotypic-phenotypic architecture of the disease. Third, evaluatingcomplexity in the pathway from SNP sets to phenotypic sets; someconnected SNP-set networks may be candidates to converge toequifinality, whereas other disjoint networks can lead to multifinality(i.e., recognizing a collection of diseases).

Finally, we carried out independent analyses to test for possibleconfirmations of the heterogeneous architecture of SZ. We performedbioinformatics analysis of genes related to each uncovered relationshipand their molecular consequences. Then, we computationally andclinically evaluated the genotypic-phenotypic relations to determinesub-classes of the disease based on whether the groups of SZ patientsvaried on a range of positive and/or negative symptoms.

d) Method

Given a genotype database from a GWAS represented as a matrix[SNPs.times.subjects], the method for dissecting the architecture of adisease is composed of 6 steps (FIG. 2), where a SNP set is a sub-matrixharboring subjects described by a set of SNPs sharing similar allelevalues:

(1) Identify SNP Sets

Use a Generalized Factorization Method (GFM) to dissect a GWAS into SNPsets (see below for a mathematical description of NMF). The GFM appliesrecurrently a basic factorization method to generate multiple matrixpartitions using various initializations with different maximum numbersof sub-matrices k (e.g., 2.Itoreq.k.Itoreq. n), where n is the number ofsubjects, and thus, avoids any pre-assumption about the ideal number ofsub-matrices (see below for a rationale about the use of unconstrainednumber of sub-matrices or clusters). Particularly, we developed a newversion of the basic bioNMF method termed Fuzzy Nonnegative MatrixFactorization method (FNMF), and used it as a default basicfactorization method. FNMF allows overlapping among sub-matrices, anddetection of outliers. For each run of the basic factorization method(2.Itoreq.k.Itoreq. n)), all sub-matrices are selected to compose afamily of genotypic SNP sets G_k={G_k_i}, where 1.Itoreq.Itoreq.k EachG_k family, as well as all families together G={G_k} for all k, mayinclude overlapped, partially redundant and different-size sub-matrices.

(2) Perform a Statistical Analysis of SNP Sets

Use the R-project package SKAT to evaluate the significance of each SNPset. We used the identity-by-state (IBS) as a kernel because theanalyzed variants are not rare but common, and therefore, using the“weighted IBS” kernel would not be adequate. Since the SNP sets canoverlap, we run each one separately. The sex and ancestry of thesubjects were used as covariates, and the default remaining parameterswere utilized.

(3) Map a Disease Risk Function

3.1) Estimate the risk of a SNP set. Incorporate a posteriori the statusof the subjects in a weighted average of epidemiological risks functionof all subjects in a particular SNP set:

$\begin{matrix}{{{Risk}\left( {{G\_ k}{\_ i}} \right)} = \frac{{\Sigma\iota\epsilon}\; {ST}{{ST}_{i}}Q_{i}}{{\Sigma\iota\epsilon}\; {ST}{{ST}_{i}}}} & (1)\end{matrix}$

with ST being the status of the instances (i.e., cases and controls) andQ the weights given by epidemiologic risk of SZ in each SNP set (e.g., 0and 1 for controls and cases; 0.01, 0.1 and 1 for cases, relatives andcontrols, respectively).

3.2) Plot the genotype risk surface of the disease. Encode each SNP setinto a 3-tuple (X, Y, Z), where SNP sets are placed along the x- andy-axis using a dendrogram based on their distances in the SNP (see step4.1, M_(SNPs)) and subject (see step 4.2, W_(subjects)) domains,respectively, and Z is the risk variable calculated in (eqn. 1).Interpolate and plot the surface by using the tgp and latticeExtrapackages in R-project, respectively.

(4) Discover and Encode Relations Among SNP Sets into TopologicallyOrganized Networks

4.1) Identify optimal and non-redundant relations between SNP sets basedon their shared SNPs and, separately, based on their shared subjects.Overlap of SNP sets refers to overlap of SNP loci, which, in most of ourcases leads also to sharing allele values. The sharing of alleles isfully true when there is overlap of both loci and subjects.

4.1.1) Co-cluster all G_k_i SNP sets within G by calculating thepairwise probability of intersection among them using the Hypergeometricstatistics (PI_(hyp)) on intersected SNPs: PI_(hyp) (G_e_q, G_r_w) (eqn.2, see below), where q and w are SNP sets generated in runs with amaximum of e and r number of sub-matrices, respectively, and p in (eqn.2) is the intersection of SNPs. Then, encode all PI_(hyp)-values, whichencompass—in some extent—the distance between SNP sets, in a square [SNPset.times.SNP set] matrix M_(SNPs).

4.1.2) Repeat the Former Procedure Based on Intersected Subjects andDetermine the M_(subjects) Matrix.

4.1.3) Eliminate highly overlapped/redundant SNP sets, which may occurdue to the repetitive application of the factorization methods, bydeleting all except one SNP set where Max(M_(SNPs)[i,j], M_(subjects)[i,j])≤δ, for all i, j indices in the matrices. Here, we used δ10E-15.

4.2) Organize SNP Sets Sharing SNPs and/or Subjects into Subnetworks.

4.2.1) For each row i and column j in M_(SNPs), M_(SNPs)[i, j]≤ϕ,connect the corresponding SNP sets with a blue line, indicating thatthey share SNPs. In our case, we established ϕ≤3E-09. This value resultsfrom adjusting typical p-value of 0.01 by the total number of pairwisecomparisons between all possible generated SNP sets [4094.times.4094, byusing the Hypergeometric-based test (eqn. 2)], likewise a Bonferronicorrection.

4.2.2) For Each Row i and Column j in M_(SNPs),M_(subjects)[i_(i, j)]≤ϕ, Connect the Corresponding SNP Sets with a RedLine, Indicating that they Share Subjects.

(5) Identify Genotype-Phenotype Latent Architectures

5.1) Create a phenotype database. Dissect the questionnaire based onDIGS and the Best Estimate Diagnosis into individual variables. Thevariables can be numerical or categorical. For efficiency, in our case,each categorical variable was re-coded into different variables withbinary values. The phenotype data was codified in a [phenotypefeatures.times.subjects] matrix, where the columns and rows correspondto subjects and phenotypic features, respectively. In our case, becausethe phenotypic features from cases are different from those from thecontrols, we only considered the cases.

5.2) Identify phenotype sets (Implemented in the PGMRA web server). Usestep 1) with the phenotype database from 5.1) instead of genotypedatabase to identify phenotypic sets, where a phenotypic set is asub-matrix harboring subjects described by a set of phenotypic featuressharing similar values (i.e., P_h_j, where j is a phenotypic setgenerated in a run with a maximum of h number of sub-matrices).

5.3) Identify genotypic-phenotypic relations. Co-cluster SNP sets withphenotype sets into relations using the Hypergeometric statistics onintersected subjects, where R_(i,j)=PI_(hyp) (G_k_i, P_h_j) (see below,eqn, 2), G_k_i, P_h_j are SNP and phenotypic sets, respectively, and pin (see below, eqn. 2) is the intersection of subjects. RelationsR_(i,j)<T constitute the genotypic-phenotypic architecture of a disease.The significance of the relations (T) was established by the p-value(PI_(hyp)) provided by the Hypergeometric-based test (see below, eqn.2).

(6) Annotate Genes, and Symptoms/Classes of Disease

6.1) Map latent architectures to the genome. For each SNP set, weanalyze all genes being affected by each of the SNPs in a SNP set. Thisanalysis includes the SNP location with respect to a gene, the type andnumber of genes being affected by one SNP (e.g., protein coding, ncRNAgenes, and pseudogenes), the possible transcripts being affected and theposition where they are affected (e.g. coding region, distance to stopcodon, splicing site, intron, UTR, etc.), and finally promoter andintergenic regions' features are inspected for annotation if the SNPdoes not overlap with a gene then regulatory. Moreover the possiblemolecular consequences of each SNP over function is provided, as wellas, the corresponding allele values. Annotation information was obtainedfrom the Haploreg DB and from the Ensembl and NCBI web services (seebelow).

Once we obtain the information described above, we generate a list ofrelevant genes that it is used to query the Nextbio web site in order tofind diseases related to each gene. NextBio uses proprietary algorithmsto calculate and rank the diseases and drugs most significantlycorrelated with a queried gene, where rank values are establishedrelative to the top-scored result (score set to 100). Therefore,although a low-scoring result might have less statistical significancecompared to the top-ranked result, it could still have real biologicalrelevance. In our case, out of all possible diseases, only thecategories “Mental Disorders” and “Brain and Nervous System Disorders”were considered from the “Disease Atlas”.

6.2) Map Latent Architectures to Disease Symptoms or Classes of Disease.

6.2.1) Characterize each phenotypic feature by the type of symptoms thatthey represent. First, explore the distribution of the phenotypicdataset by calculating the principal components (PCA, Statistic Toolbox,Matlab R2011a) of the Phenotypic sample, where the columns are subjectsand the rows are the phenotypic variables. Here we used as many PCs asneeded to account for the 75% of the sample (5 PCs). In the sample withthe phenotypic features as rows and the PCs as columns, cluster the rowsby using Hierarchical Clustering (Correlation and Maximum as inter andintra-clustering measurements, Statistic Toolbox, Matlab R2011a). Thisclustering process generates natural groups of features constitutionnatural partition hypotheses about the phenotypic features. Second,evaluate each phenotypic feature included in the phenotype databaseusing curated information from experts and the literature andindividually classify each item based on the symptoms as purely positive(1), purely negative (4), primarily positive (2) or primarily negativesymptoms (3).

6.2.2) For each phenotypic set P_h_j related to a SNP set G_k_i inR_(i,j) re-code each phenotypic feature by their positive and/ornegative symptoms in a [R_(i,j) X phenotypic feature] matrixM_(symptons).

6.2.3) Cluster the encoded features by factorizing M.sub.symptoms intosub matrices using a basic factorization method with a maximum number ofsub-matrices defined by the Cophenetic index.

6.2.4) Label the latent classes of the diseases. (The current resultsprovided 8 classes, see FIG. 5B.)

e) Mathematical Description of NMF

We consider a GWA data set consisting of a collection of NM subjectsamples (e.g., cases and controls), which we use to characterize adomain of genotypic (SNPs) states of interest. The data are representedas an nM.times.NM matrix M, whose rows contain the allele values of thenM SNPs in the NM subject samples. Using the FNMF, we find a manageablenumber of SNP sets k, positive local and linear combinations of the NMsubjects and the nM SNPs, which can be used to distinguish the geneticprofiles of the subtypes contained in the data set. Mathematically, thiscorresponds to finding an approximate factoring, M.about.WM.times.HM,where both factors have only positive entries and hence are biologicallymeaningful. WM is an nM.times.k matrix that defines the SNP setdecomposition model whose columns specify how much each of the subjectscontributes to each of the k SNP set. HM is a k.times.NM matrix whoseentries represent the SNP allele values of the k SNP sets for each ofthe NM subject samples. In our implementation either a subject or SNPcan belong to more than one SNP set.

f) Rationale for the Use of Unconstrained Number of Clusters

Although there are many indices that estimate the appropriate number ofclusters for a given partition, we previously demonstrated that they areoften constrained by the type of cluster, and metrics utilized.Therefore, it is hard to obtain a consensus from all of them, and theyvery often provide contradictory results. Moreover, given that thetarget of the method is to obtain good relations among clusters fromdifferent domains of knowledge, it is not known which cluster in onedomain will match another cluster in a different domain, and thus, themore varied the clusters, the better the chance of identifying posteriorinter-domain relations. To do so, we repeatedly applied a basicclustering method in one domain of knowledge to generate multipleclustering results using various numbers of clusters initializations(from 2 to where n is the number of observations/subjects).

g) Coincident Test Index: Co-Clustering and Establishing RelationsBetween Sets

The degree of overlapping between two SNP or phenotypic sets wasassessed by calculating the pairwise probability of intersection amongthem based on the Hypergeometric distribution (PI_(hyp)):

$\begin{matrix}{{{Risk}\left( {{G\_ k}{\_ i}} \right)} = \frac{{\Sigma\iota\epsilon}\; {ST}{{ST}_{i}}Q_{i}}{{\Sigma\iota\epsilon}\; {ST}{{ST}_{i}}}} & (1)\end{matrix}$

where p observations belong to a set of size h, and also belong to a setof size n; and g is the total number of observations. Therefore, thelower the PI_(hyp), the higher the overlapping. The (p-value of)hypergeometric “test” is used here as a measure of association strength.The real test (p-value) of genotypic-phenotypic relationship wasprovided through the permutation procedure.

h) Permutation Test for Genotypic-Phenotypic Relations

Statistical significance reported values were obtained by 4000independent permutations due to the comparisons between all possiblegenerated SNP sets (i.e., 4094, from 2 to √n), and possible overlappedSNP sets here identified were generated as following: a) assign randomsubjects to a phenotypic cluster of random size; b) assign randomsubjects to a genotype cluster (set) of random size; c) calculate theHypergeometric statistic (PI_(hyp), eqn 2) between the two clusters andaccumulate the value. These values form an empirical null distributionof PI_(hyp) used to calculate the empirical p-value of an identifiedrelation. All optimal relations had empirical p-value≤value<4.7E-03.

i) Resampling Statistics of the NMF Sets

To guarantee the submatrices converge to the same solution and, giventhe non-deterministic nature of NMF and its dependence on theinitialization of the W and H vectors, we run it 40 times for any kmaximum number of allowed submatrices with different randominitializations of the vectors to select those that that bestapproximates the input matrix. Besides, to estimate the precision ofsample statistics of the SNP sets (variance of the W and H vectors) weuse a leave-one-out technique (jackknifing) 1000 times on the SNP domainand obtained a 94% support for all identified sets with an averagevariance of c.a.±0.5% of their corresponding W and H vectors. Finally,we already modified this sampling technique to ensure the occurrence ofthe remaining sets after a leave-one-set-out and applied to our currentsample with >90% of support.

j) Data Reduction

Data reduction was not applied because many Principal Components (PCs)were required in this study, consistent with the demonstration thatclustering with the PCs instead of the original variables does notnecessarily improve, and often degrades, cluster quality andinterpretability. Moreover, likewise in phenomics, partially correlatedvariables reinforce the association and clarify the symptomidentification process. Therefore, we used initially 93 phenotypicfeatures listed in Appendix I, catalog of phenotypic features.

Briefly, phenotypic features used in the search process included allavailable data from the interviews. That is, replies to DIGS as well asto the Best Estimate Diagnosis code sheet submitted by GAIN/NONGAIN todbGaP. Unbiased compilation of all of the data resulted in an initialset of 93 features. To capture items specific for positive and negativeschizophrenia and avoid symptoms with affective elements, symptomsreported by acutely psychotic patients, and redundant items the originalset of was pruned based on authors clinical experience, andcomputational feature validation (above in Method, step 6.2.1).

3. Bioinformatics Analysis: Genotypic Organization of the SZArchitecture Accounts for Multiple Genetic Sources of the Disease

Given that genotypic SZ architecture is composed of multiple networks,we matched each SNP set composing these networks with the correspondinggenomic location of their SNPs, and in turn, with the mapped genes (FIG.5A, Table 2) to investigate what these SNP sets represent in terms ofgenomic information. We uncovered a list of genes with many differentfunctions and distinct roles in different molecular networks (Tables2-4).

4. A Single SNP Set can Map Different Classes of Genes, Located inDifferent Chromosomes, and Distinct Types of Genetic Variants

The uncovered SNP sets contain SNPs that map gene, promoter andintergenic regions (IGRs) located anywhere in the genome, without beingconstrained by genomic features such as a specific gene or haplotype(28). For example, SNP set 81_13 contains SNPs in chromosomes 8 and 16,whereas SNP set 42_37 has SNPs located in chromosomes 2 and 11 (FIG. 5A,Table 2). SNP set 75_67 has SNPs in chromosomes 4, 8, 15, and 16, amongothers, and maps >30 genes, as expected by its generality (FIG. 5A,Table 2). The latter SNP set is in the same network as SNP sets 56_30,76_74 and 81_13, and thus shares some genes with them. Despite being inthe same network, the last three SNP sets map to particular genesspecific to each of them (FIG. 5A, Table 2).

In addition to mapping genes in different locations, SNP variants withinthe SNP sets affect distinct classes of genes including protein-coding,non-coding (ncRNA) genes, and pseudogenes, with different molecularconsequences depending on the altered region (coding, UTRs, introns,Table 4). For example, only 25% of SNPs in SNP set 75_67 affectprotein-coding genes, which are the targets most often considered ingenetic studies of diseases, whereas another 25% of SNPs affect ncRNAs(lincRNAs, antisense RNAs, miRNAs). One of these lincRNAs is SOX2-OT,which is associated with >15 possible transcripts (Table 4); it iscontained inside the SOX2 transcription factor that is predominantlyexpressed in the human brain where SOX2-OT is also highly enriched.

TABLE 4 Molecular Consequences of SNP Variants. Regulatory elementEnsembl gene EntrezGene Variation Group Location Allele Gene (Ensembl)name UniProt ID ID rs10488268  9_9 7: 83733446 T ENSG00000075213 SEMA3ASEMA3A 10371 rs11631112  9_9 15: 88659906 T ENSG00000140538 NTRK3 NTRK34916 rs13228082  9_9 7: 83726968 G ENSG00000075213 SEMA3A SEMA3A 10371rs16941261  9_9 15: 88655520 C ENSG00000140538 NTRK3 NTRK3 4916rs17298417  9_9 7: 83730162 C ENSG00000075213 SEMA3A SEMA3A 10371rs3784405  9_9 15: 88688010 C ENSG00000140538 NTRK3 NTRK3 4916 rs3784405 9_9 15: 88688010 C ENSG00000259183 RP11-356B18.1 rs3801629  9_9 7:83734593 G ENSG00000075213 SEMA3A SEMA3A 10371 rs6496466  9_9 15:88717708 C ENSG00000140538 NTRK3 NTRK3 4916 rs7806871  9_9 7: 83727983 GENSG00000075213 SEMA3A SEMA3A 10371 rs994068  9_9 15: 88666646 CENSG00000140538 NTRK3 NTRK3 4916 rs995866  9_9 7: 83745039 CENSG00000075213 SEMA3A SEMA3A 10371 rs11630338  9_9 15: 88661632 CENSG00000140538 NTRK3 NTRK3 4916 rs2114252  9_9 15: 88664676 AENSG00000140538 NTRK3 NTRK3 4916 rs3801616  9_9 7: 83721051 AENSG00000075213 SEMA3A SEMA3A 10371 rs4887364  9_9 15: 88660115 CENSG00000140538 NTRK3 NTRK3 4916 rs727650  9_9 7: 83735838 GENSG00000075213 SEMA3A SEMA3A 10371 rs727651  9_9 7: 83735893 GENSG00000075213 SEMA3A SEMA3A 10371 rs764116  9_9 7: 83738481 AENSG00000075213 SEMA3A SEMA3A 10371 rs991728  9_9 15: 88662946 GENSG00000140538 NTRK3 NTRK3 4916 rs11159957 10_4 14: 90715972 Ars11621045 10_4 14: 90714003 A ENSR00001459588 rs11621045 10_4 14:90714003 A rs11623741 10_4 14: 90804474 G rs11628812 10_4 14: 90713720 Crs7150093 10_4 14: 90724661 G ENSG00000100764 PSMC1 PSMC1 5700 rs715469510_4 14: 90795705 G ENSG00000119720 C14orf102 C14ORF102 55051 rs1115995712_11 14: 90715972 A rs11621045 12_11 14: 90714003 A ENSR00001459588rs11621045 12_11 14: 90714003 A rs11623741 12_11 14: 90804474 Grs11626869 12_11 14: 90788985 G ENSG00000119720 C14orf102 C14ORF10255051 rs11628812 12_11 14: 90713720 C rs7150093 12_11 14: 90724661 GENSG00000100764 PSMC1 PSMC1 5700 rs7154695 12_11 14: 90795705 GENSG00000119720 C14orf102 C14ORF102 55051 rs11159956 12_11 14: 90715890C rs17188598 12_11 14: 90722473 T ENSG00000100764 PSMC1 PSMC1 5700rs3783838 12_11 14: 90733012 G ENSG00000100764 PSMC1 PSMC1 5700rs7146640 12_11 14: 90720114 A ENSG00000100764 PSMC1 PSMC1 5700rs10030713 12_2 4: 95238536 C ENSG00000163106 HPGDS PGDS 27306rs12646184 12_2 4: 95183216 T ENSG00000163104 SMARCAD1 SMARCAD1 56916rs17021364 12_2 4: 95047893 C ENSR00001433195 rs17021364 12_2 4:95047893 C ENSG00000246541 RP11-363G15.2 rs2059606 12_2 4: 95255278 AENSG00000163106 HPGDS PGDS 27306 rs2664871 12_2 4: 95146281 TENSG00000163104 SMARCAD1 SMARCAD1 56916 rs6532482 12_2 4: 95277414 Grs6839224 12_2 4: 95279214 G rs11097407 12_2 4: 95146135 CENSG00000163104 SMARCAD1 SMARCAD1 56916 rs1991316 12_2 4: 95268272 TENSG00000163106 HPGDS PGDS 27306 rs2059605 12_2 4: 95255212 CENSG00000163106 HPGDS PGDS 27306 rs2087170 12_2 4: 95162960 GENSG00000163104 SMARCAD1 SMARCAD1 56916 rs2632401 12_2 4: 95147055 GENSG00000163104 SMARCAD1 SMARCAD1 56916 rs1144918 13_12 14: 89102558 CENSG00000165521 EML5 EML5 161436 rs11845781 13_12 14: 89276431 Trs1287660 13_12 14: 89286845 G ENSG00000165533 TTC8 TTC8 123016rs1287660 13_12 14: 89286845 G ENSG00000200653 U4 rs12880096 13_12 14:89218815 C ENSG00000165521 EML5 EML5 161436 rs1956411 13_12 14: 89134360T ENSR00001459464 rs1956411 13_12 14: 89134360 T ENSG00000165521 EML5EML5 161436 rs4904448 13_12 14: 88852166 A ENSR00000099273 rs490444813_12 14: 88852166 A ENSG00000042317 SPATA7 SPATA7 55812 rs7147796 13_1214: 89228569 G ENSG00000165521 EML5 EML5 161436 rs10132509 13_12 14:89203781 G ENSG00000165521 EML5 EML5 161436 rs10140896 13_12 14:89218538 G ENSG00000165521 EML5 EML5 161436 rs1287825 13_12 14: 89105536G ENSG00000165521 EML5 EML5 161436 rs3784405 14_6 15: 88688010 CENSG00000140538 NTRK3 NTRK3 4916 rs3784405 14_6 15: 88688010 CENSG00000259183 RP11-356B18.1 rs994068 14_6 15: 88666646 CENSG00000140538 NTRK3 NTRK3 4916 rs1105442 14_6 15: 88724647 TENSG00000140538 NTRK3 NTRK3 4916 rs11630338 14_6 15: 88661632 CENSG00000140538 NTRK3 NTRK3 4916 rs11631112 14_6 15: 88659906 TENSG00000140538 NTRK3 NTRK3 4916 rs12911150 14_6 15: 88668691 GENSG00000140538 NTRK3 NTRK3 4916 rs16941261 14_6 15: 88655520 CENSG00000140538 NTRK3 NTRK3 4916 rs2114252 14_6 15: 88664676 AENSG00000140538 NTRK3 NTRK3 4916 rs4887364 14_6 15: 88660115 CENSG00000140538 NTRK3 NTRK3 4916 rs6496466 14_6 15: 88717708 CENSG00000140538 NTRK3 NTRK3 4916 rs991728 14_6 15: 88662946 GENSG00000140538 NTRK3 NTRK3 4916 rs10030713 16_10 4: 95238536 CENSG00000163106 HPGDS PGDS 27306 rs12646184 16_10 4: 95183216 TENSG00000163104 SMARCAD1 SMARCAD1 56916 rs17021364 16_10 4: 95047893 CENSR00001433195 rs17021364 16_10 4: 95047893 C ENSG00000246541RP11-363G15.2 rs2059606 16_10 4: 95255278 A ENSG00000163106 HPGDS PGDS27306 rs2664871 16_10 4: 95146281 T ENSG00000163104 SMARCAD1 SMARCAD156916 rs6532482 16_10 4: 95277414 G rs6839224 16_10 4: 95279214 Grs11097407 16_10 4: 95146135 C ENSG00000163104 SMARCAD1 SMARCAD1 56916rs1991316 16_10 4: 95268272 T ENSG00000163106 HPGDS PGDS 27306 rs205960516_10 4: 95255212 C ENSG00000163106 HPGDS PGDS 27306 rs2059606 16_10 4:95255278 A ENSG00000163106 HPGDS PGDS 27306 rs2087170 16_10 4: 95162960G ENSG00000163104 SMARCAD1 SMARCAD1 56916 rs2632401 16_10 4: 95147055 GENSG00000163104 SMARCAD1 SMARCAD1 56916 rs10819000 19_2 9: 127619553 GENSG00000136918 WDR38 WDR38 401551 rs10819000 19_2 9: 127619553 GENSG00000136942 RPL35 RPL35 11224 rs10819000 19_2 9: 127619553 GENSG00000136950 ARPC5L ARPC5L 81873 rs10819019 19_2 9: 127750409 GENSG00000173611 SCAI SCAI 286205 rs10986471 19_2 9: 127635713 GENSG00000136935 GOLGA1 GOLGA1 2800 rs10986471 19_2 9: 127635713 GENSG00000136950 ARPC5L ARPC5L 81873 rs388704 19_2 9: 127801357 TENSG00000173611 SCAI SCAI 286205 rs634710 19_2 9: 127661645 AENSG00000136935 GOLGA1 GOLGA1 2800 rs634710 19_2 9: 127661645 AENSG00000264641 AL354928.1 rs640052 19_2 9: 127647800 A ENSG00000136935GOLGA1 GOLGA1 2800 rs640052 19_2 9: 127647800 A ENSG00000199313 U4rs687434 19_2 9: 127643456 C ENSG00000136935 GOLGA1 GOLGA1 2800 rs68743419_2 9: 127643456 C ENSG00000136950 ARPC5L ARPC5L 81873 rs7031479 19_29: 127686126 T ENSG00000136935 GOLGA1 GOLGA1 2800 rs7022663 19_2 9:127673385 C ENSG00000136935 GOLGA1 GOLGA1 2800 rs13413863 21_8 2:22615313 G ENSG00000234207 AC096570.2 rs13424767 21_8 2: 22612275 CENSG00000231200 AC068490.2 rs13424767 21_8 2: 22612275 C ENSG00000234207AC096570.2 rs1396725 21_8 2: 22612638 A ENSG00000231200 AC068490.2rs1396725 21_8 2: 22612638 A ENSG00000234207 AC096570.2 rs1509355 21_82: 22613819 T ENSG00000231200 AC068490.2 rs1509355 21_8 2: 22613819 TENSG00000234207 AC096570.2 rs1509360 21_8 2: 22616777 A ENSG00000231200AC068490.2 rs1509360 21_8 2: 22616777 A ENSG00000234207 AC096570.2rs1949038 21_8 2: 22616534 C ENSG00000231200 AC068490.2 rs1949038 21_82: 22616534 C ENSG00000234207 AC096570.2 rs6741194 21_8 2: 22616209 TENSG00000231200 AC068490.2 rs6741194 21_8 2: 22616209 T ENSG00000234207AC096570.2 rs6749647 21_8 2: 22618537 T ENSG00000231200 AC068490.2rs6749647 21_8 2: 22618537 T ENSG00000234207 AC096570.2 rs9308959 21_82: 22553001 T ENSG00000231200 AC068490.2 rs6743484 21_8 2: 22553712 TENSG00000231200 AC068490.2 rs7569716 21_8 2: 22568713 T ENSG00000231200AC068490.2 rs13413863 22_11 2: 22615313 G ENSG00000234207 AC096570.2rs13424767 22_11 2: 22612275 C ENSG00000231200 AC068490.2 rs1342476722_11 2: 22612275 C ENSG00000234207 AC096570.2 rs1396725 22_11 2:22612638 A ENSG00000231200 AC068490.2 rs1396725 22_11 2: 22612638 AENSG00000234207 AC096570.2 rs1509355 22_11 2: 22613819 T ENSG00000231200AC068490.2 rs1509355 22_11 2: 22613819 T ENSG00000234207 AC096570.2rs1509360 22_11 2: 22616777 A ENSG00000231200 AC068490.2 rs1509360 22_112: 22616777 A ENSG00000234207 AC096570.2 rs1949038 22_11 2: 22616534 CENSG00000231200 AC068490.2 rs1949038 22_11 2: 22616534 C ENSG00000234207AC096570.2 rs6741194 22_11 2: 22616209 T ENSG00000231200 AC068490.2rs6741194 22_11 2: 22616209 T ENSG00000234207 AC096570.2 rs6749647 22_112: 22618537 T ENSG00000231200 AC068490.2 rs6749647 22_11 2: 22618537 TENSG00000234207 AC096570.2 rs9308959 22_11 2: 22553001 T ENSG00000231200AC068490.2 rs1605834 22_11 2: 22576100 G ENSG00000231200 AC068490.2rs7569716 22_11 2: 22568713 T ENSG00000231200 AC068490.2 rs6743484 22_112: 22553712 T ENSG00000231200 AC068490.2 rs1325566 25_10 X: 55791497 Trs1325567 25_10 X: 55791441 C rs1325572 25_10 X: 55828681 T rs147376125_10 X: 55748820 G ENSG00000083750 RRAGB RRAGB 10325 rs2104429 25_10 X:55827933 A rs5914459 25_10 X: 55823342 C rs5914490 25_10 X: 55873522 Crs942846 25_10 X: 55841702 C rs1075145 25_10 X: 55823685 T rs239684131_22 6: 47862920 T ENSG00000244694 PTCHD4 PTCHD4 442213 rs473606 31_226: 47808177 T rs9395325 31_22 6: 47854343 T ENSG00000244694 PTCHD4PTCHD4 442213 rs1328974 31_22 6: 47833487 C rs2022333 31_22 6: 47864831A ENSG00000244694 PTCHD4 PTCHD4 442213 rs6912591 31_22 6: 47853375 GENSG00000244694 PTCHD4 PTCHD4 442213 rs7756106 31_22 6: 47852752 CENSG00000244694 PTCHD4 PTCHD4 442213 rs5932754 41_12 X: 129515071 TENSG00000147262 GPR119 GPR119 139760 rs5977248 41_12 X: 129501487 TENSG00000102078 SLC25A14 SLC25A14 9016 rs4830188 41_12 X: 129514423 TENSG00000147262 GPR119 GPR119 139760 rs10502161 42_37 11: 112843425 GENSG00000149294 NCAM1 NCAM1 4684 rs10502161 42_37 11: 112843425 GENSG00000238998 U7 rs10502170 42_37 11: 113040118 G ENSG00000149294NCAM1 NCAM1 4684 rs11214533 42_37 11: 113048466 C ENSR00001573647rs11214533 42_37 11: 113048466 C ENSG00000149294 NCAM1 NCAM1 4684rs1196185 42_37 2: 182884959 A ENSG00000150722 PPP1R1C LOC151242 151242rs2011507 42_37 11: 112988280 C ENSG00000149294 NCAM1 NCAM1 4684rs2212450 42_37 11: 112826867 C ENSG00000247416 RP11-629G13.1 rs270166442_37 2: 182908664 A ENSG00000150722 PPP1R1C LOC151242 151242 rs270166442_37 2: 182908664 A ENSG00000222418 RNA5SP113 rs6589360 42_37 11:113050292 T ENSG00000149294 NCAM1 NCAM1 4684 rs6732434 42_37 2:182901257 G ENSG00000150722 PPP1R1C LOC151242 151242 rs7110628 42_37 11:112842988 G ENSG00000149294 NCAM1 NCAM1 4684 rs12575544 42_37 11:112918985 A ENSG00000149294 NCAM1 NCAM1 4684 rs1273044 42_37 11:112993848 C ENSG00000149294 NCAM1 NCAM1 4684 rs1245133 42_37 11:113011721 G ENSG00000149294 NCAM1 NCAM1 4684 rs17114705 42_37 11:112899832 A ENSG00000149294 NCAM1 NCAM1 4684 rs17114685 42_37 11:112889330 T ENSG00000149294 NCAM1 NCAM1 4684 rs12272966 42_37 11:113034787 T ENSG00000149294 NCAM1 NCAM1 4684 rs17114687 42_37 11:112889357 G ENSG00000149294 NCAM1 NCAM1 4684 rs17114757 42_37 11:112951637 T ENSG00000149294 NCAM1 NCAM1 4684 rs17582738 42_37 11:112840745 T ENSG00000149294 NCAM1 NCAM1 4684 rs17114689 42_37 11:112894450 G ENSG00000149294 NCAM1 NCAM1 4684 rs1436109 42_37 11:112991618 T ENSG00000149294 NCAM1 NCAM1 4684 rs1196160 42_37 2:182928012 A ENSG00000150722 PPP1R1C LOC151242 151242 rs1196155 42_37 2:182921272 C ENSG00000150722 PPP1R1C LOC151242 151242 rs1196183 42_37 2:182888983 T ENSG00000150722 PPP1R1C LOC151242 151242 rs5932896 51_28 X:130470292 T ENSG00000147255 IGSF1 IGSF1 3547 rs4462056 51_28 X:130438580 A ENSG00000147255 IGSF1 IGSF1 3547 rs4415478 51_28 X:130438656 A ENSG00000147255 IGSF1 IGSF1 3547 rs10502161 52_42 11:112843425 G ENSG00000149294 NCAM1 NCAM1 4684 rs10502161 52_42 11:112843425 G ENSG00000238998 U7 rs10502170 52_42 11: 113040118 GENSG00000149294 NCAM1 NCAM1 4684 rs11214533 52_42 11: 113048466 CENSR00001573647 rs17582738 52_42 11: 112840745 T ENSG00000149294 NCAM1NCAM1 4684 rs2212450 52_42 11: 112826867 C ENSG00000247416 RP11-629G13.1rs7110628 52_42 11: 112842988 G ENSG00000149294 NCAM1 NCAM1 4684rs12575544 52_42 11: 112918985 A ENSG00000149294 NCAM1 NCAM1 4684rs1273044 52_42 11: 112993848 C ENSG00000149294 NCAM1 NCAM1 4684rs17114705 52_42 11: 112899832 A ENSG00000149294 NCAM1 NCAM1 4684rs1245133 52_42 11: 113011721 G ENSG00000149294 NCAM1 NCAM1 4684rs12272966 52_42 11: 113034787 T ENSG00000149294 NCAM1 NCAM1 4684rs17114685 52_42 11: 112889330 T ENSG00000149294 NCAM1 NCAM1 4684rs17114687 52_42 11: 112889357 G ENSG00000149294 NCAM1 NCAM1 4684rs17114757 52_42 11: 112951637 T ENSG00000149294 NCAM1 NCAM1 4684rs6589360 52_42 11: 113050292 T ENSG00000149294 NCAM1 NCAM1 4684rs17114689 52_42 11: 112894450 G ENSG00000149294 NCAM1 NCAM1 4684rs2725046 54_51 8: 4467853 G ENSG00000183117 CSMD1 CSMD1 64478 rs138225054_51 8: 4465300 T ENSG00000183117 CSMD1 CSMD1 64478 rs2617104 54_51 8:4467788 C ENSG00000183117 CSMD1 CSMD1 64478 rs2725037 54_51 8: 4471486 GENSG00000183117 CSMD1 CSMD1 64478 rs2725045 54_51 8: 4467334 TENSG00000183117 CSMD1 CSMD1 64478 rs10791112 56_19 11: 130870215 TENSR00000571552 rs10791112 56_19 11: 130870215 T ENSG00000242673Metazoa_SRP rs10894294 56_19 11: 130830748 A rs1433976 56_19 11:130875123 G ENSG00000242673 Metazoa_SRP rs1991899 56_19 11: 130801649 Grs10874067 56_30 1: 80207766 T rs1524183 56_30 1: 80179889 C rs159186556_30 1: 97177244 G rs1591866 56_30 1: 97177209 G rs4402575 56_30 16:20297138 A rs6497455 56_30 16: 20283920 C rs6497465 56_30 16: 20288797 Ars6699242 56_30 1: 97258468 A ENSG00000117569 PTBP2 PTBP2 58155rs7191525 56_30 16: 20276957 G rs8050244 56_30 16: 20277579 T rs805489856_30 16: 20290454 C rs4581094 58_29 8: 66065387 A ENSG00000239261RPL31P41 rs4599855 58_29 8: 66088232 C rs4737704 58_29 8: 66072703 TENSG00000239261 RPL31P41 rs6982800 58_29 8: 66074511 A rs6998613 58_298: 66074310 C rs12544654 58_29 8: 66102770 C rs231150 59_48 8: 116420327T ENSG00000104447 TRPS1 TRPS1 7227 rs6047529 59_48 20: 2215286 Crs6137352 59_48 20: 2198288 A ENSG00000226644 RP11-128M1.1 388780rs2049863 59_49 8: 116409435 T rs231146 59_50 8: 116416989 GENSG00000104447 TRPS1 TRPS1 7227 rs6082408 59_51 20: 2192516 CENSG00000226644 RP11-128M1.1 388780 rs6082421 59_52 20: 2197908 AENSG00000226644 RP11-128M1.1 388780 rs5932896 61_39 X: 130470292 TENSG00000147255 IGSF1 IGSF1 3547 rs4462056 61_39 X: 130438580 AENSG00000147255 IGSF1 IGSF1 3547 rs4415478 61_39 X: 130438656 AENSG00000147255 IGSF1 IGSF1 3547 rs2208760 65_25 20: 18910490 Trs4814813 65_25 20: 18930034 G rs6045692 65_25 20: 18901412 T rs604570665_25 20: 18929348 T rs1555510 65_25 20: 18942562 C rs11632716 71_55 15:88360283 C ENSR00001454866 rs16940789 71_55 15: 88322461 A rs198682671_55 15: 88327131 C rs4243096 71_55 15: 88366975 C rs4887326 71_55 15:88341400 G rs7166186 71_55 15: 88345483 T rs10791112 75_31 11: 130870215T ENSR00000571552 rs10791112 75_31 11: 130870215 T ENSG00000242673Metazoa_SRP rs10894294 75_31 11: 130830748 A rs1433976 75_31 11:130875123 G ENSG00000242673 Metazoa_SRP rs1991899 75_31 11: 130801649 Grs514235 75_31 1: 93438456 C ENSG00000239710 Metazoa_SRP rs514235 75_311: 93438456 C ENSG00000252121 U6 rs521428 75_31 1: 93445497 AENSG00000238787 AC093577.1 rs521428 75_31 1: 93445497 A ENSG00000239710Metazoa_SRP rs660870 75_31 1: 93445417 A ENSG00000238787 AC093577.1rs660870 75_31 1: 93445417 A ENSG00000239710 Metazoa_SRP rs1079110975_31 11: 130850377 G rs11632716 75_67 15: 88360283 C rs11785991 75_678: 51750040 A rs11945291 75_67 4: 98184296 G ENSG00000163116 STPG2C4ORF37 285555 rs12908584 75_67 15: 86643080 G ENSG00000260477RP11-553E24.2 rs134432 75_67 22: 35588844 G ENSG00000233080 CTA-714B7.5rs134432 75_67 22: 35588844 G ENSG00000243453 COX7BP1 rs1805610 75_67 3:180772241 T ENSG00000242808 SOX2-OT 347689 rs1805610 75_67 3: 180772241T ENSG00000243341 RP11-436A20.3 rs1979268 75_67 12: 10776513 GENSG00000060140 STYK1 STYK1 55359 rs1986826 75_67 15: 88327131 Crs2161850 75_67 8: 30577906 C ENSR00001440140 rs2161850 75_67 8:30577906 C ENSG00000104687 GSR GSR 2936 rs2317837 75_67 16: 82324743 Trs2763529 75_67 14: 103654939 T ENSG00000251533 LINC00605 100131366rs2763529 75_67 14: 103654939 T ENSG00000259525 GCSHP2 rs3888124 75_678: 42285336 C ENSG00000168575 SLC20A2 SLC20A2 6575 rs4243096 75_67 15:88366975 C rs4402575 75_67 16: 20297138 A rs4603135 75_67 1: 116171383 Trs4699310 75_67 4: 98147844 T ENSG00000163116 STPG2 C4ORF37 285555rs4732942 75_67 8: 29297518 C rs4887326 75_67 15: 88341400 G rs649745575_67 16: 20283920 C rs6497465 75_67 16: 20288797 A rs6984059 75_67 8:52148019 C rs7006725 75_67 8: 53055353 A ENSG00000147488 ST18 ST18 9705rs717509 75_67 8: 51566749 G ENSG00000147481 SNTG1 SNTG1 54212 rs719152575_67 16: 20276957 G rs7819847 75_67 8: 50367785 C rs7832529 75_67 8:42306813 C ENSG00000168575 SLC20A2 SLC20A2 6575 rs8050244 75_67 16:20277579 T rs8054898 75_67 16: 20290454 C rs900237 75_67 8: 49596141 CENSG00000233858 AC026904.1 rs900237 75_67 8: 49596141 C ENSG00000253608RP11-770E5.1 rs962392 75_67 10: 108014282 T rs9917982 75_67 4: 98107638T ENSG00000163116 STPG2 C4ORF37 285555 rs7009058 75_67 8: 51493707 CENSG00000147481 SNTG1 SNTG1 54212 rs5932896 76_63 X: 130470292 TENSG00000147255 IGSF1 IGSF1 3547 rs4462056 X: 130438580 AENSG00000147255 IGSF1 IGSF1 3547 rs4415478 X: 130470292 TENSG00000147255 IGSF1 IGSF1 3547 rs11945291 76_74 4: 98184296 GENSG00000163116 STPG2 C4ORF37 285555 rs2763529 76_74 14: 103654939 TENSG00000251533 LINC00605 100131366 rs2763529 76_74 14: 103654939 TENSG00000259525 GCSHP2 rs2875373 76_74 4: 24700151 T rs4581094 76_74 8:66065387 A ENSG00000239261 RPL31P41 rs4697472 76_74 4: 24698303 Crs4699310 76_74 4: 98147844 T ENSG00000163116 STPG2 C4ORF37 285555rs4737704 76_74 8: 66072703 T ENSG00000239261 RPL31P41 rs6812181 76_744: 24711351 T rs6888272 76_74 5: 73355560 T rs6982800 76_74 8: 66074511A rs6998613 76_74 8: 66074310 C rs900237 76_74 8: 49596141 CENSG00000233858 AC026904.1 rs900237 76_74 8: 49596141 C ENSG00000253608RP11-770E5.1 rs9917982 76_74 4: 98107638 T ENSG00000163116 STPG2 C4ORF37285555 rs9938516 76_74 16: 47926261 C ENSG00000261231 RP11-523L20.2rs2725046 77_5 8: 4467853 G ENSG00000183117 CSMD1 CSMD1 64478 rs138225077_5 8: 4465300 T ENSG00000183117 CSMD1 CSMD1 64478 rs2617104 77_5 8:4467788 C ENSG00000183117 CSMD1 CSMD1 64478 rs2725037 77_5 8: 4471486 GENSG00000183117 CSMD1 CSMD1 64478 rs2725045 77_5 8: 4467334 TENSG00000183117 CSMD1 CSMD1 64478 rs4402575 81_13 16: 20297138 Ars6497455 81_13 16: 20283920 C rs6497465 81_13 16: 20288797 A rs698405981_13 8: 52148019 C rs717509 81_13 8: 51566749 G ENSG00000147481 SNTG1SNTG1 54212 rs7191525 81_13 16: 20276957 G rs8050244 81_13 16: 20277579T rs8054898 81_13 16: 20290454 C rs11785991 81_13 8: 51750040 Ars7009058 81_13 8: 51493707 C ENSG00000147481 SNTG1 SNTG1 54212rs13413863 81_3 2: 22615313 G ENSG00000234207 AC096570.2 rs13424767 81_32: 22612275 C ENSG00000231200 AC068490.2 rs13424767 81_3 2: 22612275 CENSG00000234207 AC096570.2 rs1396725 81_3 2: 22612638 A ENSG00000231200AC068490.2 rs1396725 81_3 2: 22612638 A ENSG00000234207 AC096570.2rs1509355 81_3 2: 22613819 T ENSG00000231200 AC068490.2 rs1509355 81_32: 22613819 T ENSG00000234207 AC096570.2 rs1509360 81_3 2: 22616777 AENSG00000231200 AC068490.2 rs1509360 81_3 2: 22616777 A ENSG00000234207AC096570.2 rs1949038 81_3 2: 22616534 C ENSG00000231200 AC068490.2rs1949038 81_3 2: 22616534 C ENSG00000234207 AC096570.2 rs6741194 81_32: 22616209 T ENSG00000231200 AC068490.2 rs6741194 81_3 2: 22616209 TENSG00000234207 AC096570.2 rs6749647 81_3 2: 22618537 T ENSG00000231200AC068490.2 rs6749647 81_3 2: 22618537 T ENSG00000234207 AC096570.2rs9308959 81_3 2: 22553001 T ENSG00000231200 AC068490.2 rs1605834 81_32: 22576100 G ENSG00000231200 AC068490.2 rs6743484 81_3 2: 22553712 TENSG00000231200 AC068490.2 rs7569716 81_3 2: 22568713 T ENSG00000231200AC068490.2 rs12956646 81_73 18: 24685369 C ENSG00000154080 CHST9 CHST983539 rs12956646 81_73 18: 24685369 C ENSG00000260372 CHST9-AS1 147429rs12956990 81_73 18: 24713270 C ENSG00000154080 CHST9 CHST9 83539rs12956990 81_73 18: 24713270 C ENSG00000260372 CHST9-AS1 147429rs2030234 81_73 11: 86965391 G ENSG00000166575 TMEM135 TMEM135 65084rs2030234 81_73 11: 86965391 G ENSG00000213287 RP11-680L20.1 rs257218981_73 15: 33763472 G ENSG00000198838 RYR3 RYR3 6263 rs61552 81_73 11:86920178 G ENSG00000166575 TMEM135 TMEM135 65084 rs7240658 81_73 18:24687347 A ENSG00000154080 CHST9 CHST9 83539 rs7240658 81_73 18:24687347 A ENSG00000260372 CHST9-AS1 147429 rs919140 81_73 18: 24689706C ENSG00000154080 CHST9 CHST9 83539 rs11235109 81_73 11: 87059742 Grs186198 81_73 11: 86911919 C ENSG00000166575 RYR3 RYR3 6263 rs257217581_73 15: 33777705 C ENSG00000198838 RYR3 RYR3 6263 rs4770836 83_41 13:26037909 C ENSR00000513160 rs668001 83_41 13: 26005056 C ENSG00000132932ATP8A2 ATP8A2 51761 rs668001 83_41 13: 26005056 C ENSG00000132932 ATP8A2ATP8A2 51761 rs640894 83_41 13: 26006474 G ENSG00000132932 ATP8A2 ATP8A251761 rs12956646 85_23 18: 24685369 C ENSG00000154080 CHST9 CHST9 83539rs12956646 85_23 18: 24685369 C ENSG00000260372 CHST9-AS1 147429rs12956990 85_23 18: 24713270 C ENSG00000154080 CHST9 CHST9 83539rs12956990 85_23 18: 24713270 C ENSG00000260372 CHST9-AS1 147429rs7240658 85_23 18: 24687347 A ENSG00000154080 CHST9 CHST9 83539rs7240658 85_23 18: 24687347 A ENSG00000260372 CHST9-AS1 147429 rs91914085_23 18: 24689706 C ENSG00000154080 CHST9 CHST9 83539 rs919140 85_2318: 24689706 C ENSG00000260372 CHST9-AS1 147429 rs1146745 85_84 3:84904026 T ENSG00000242641 RP11-735B13.1 440970 rs1248821 85_84 3:84930747 C ENSG00000242339 RP11-735B13.2 rs385115 85_84 3: 84892835 AENSG00000242641 RP11-735B13.1 440970 rs1248845 85_84 3: 84871763 AENSG00000242641 RP11-735B13.1 440970 rs12430088 87_26 13: 101704076 TENSG00000233009 NALCN-AS1 100885778 rs3751403 87_26 13: 101701747 TENSR00001511846 rs3751403 87_26 13: 101701747 T ENSG00000102452 NALCNNALCN 259232 rs3751403 87_26 13: 101701747 T ENSG00000233009 NALCN-AS1100885778 rs638732 87_26 13: 101709598 G ENSG00000102452 NALCN NALCN259232 rs638732 87_26 13: 101709598 G ENSG00000233009 NALCN-AS1100885778 rs9554752 87_26 13: 101726313 T ENSG00000102452 NALCN NALCN259232 rs7986657 87_26 13: 101736999 G ENSG00000102452 NALCN NALCN259232 rs10782945 87_84 1: 93304272 T ENSG00000122406 RPL5 RPL5 6083rs10782945 87_84 1: 93304272 T ENSG00000154511 FAM69A FAM69A 388650rs10782945 87_84 1: 93304272 T ENSG00000206680 SNORD21 6083 rs1078294587_84 1: 93304272 T ENSG00000207523 SNORA66 26782 rs10782945 87_84 1:93304272 T ENSG00000251795 SNORA66 rs11164835 87_84 1: 93379093 AENSG00000154511 FAM69A FAM69A 388650 rs12066638 87_84 1: 93375391 GENSR00001522451 rs12745968 87_84 1: 93401837 G ENSG00000154511 FAM69AFAM69A 388650 rs12745968 87_84 1: 93401837 G ENSG00000229052RP11-386123.1 rs35183060 87_84 1: 93346928 T ENSG00000154511 FAM69AFAM69A 388650 rs6604026 87_84 1: 93303603 C ENSR00000540793 rs660402687_84 1: 93303603 C ENSG00000122406 RPL5 RPL5 6083 rs6604026 87_84 1:93303603 C ENSG00000154511 FAM69A FAM69A 388650 rs6604026 87_84 1:93303603 C ENSG00000206680 SNORD21 6083 rs6604026 87_84 1: 93303603 CENSG00000207523 SNORA66 26782 rs6604026 87_84 1: 93303603 CENSG00000251795 SNORA66 rs9651257 87_84 1: 93385136 C ENSG00000154511FAM69A FAM69A 388650 rs10874753 87_84 1: 93429087 A ENSG00000154511FAM69A FAM69A 388650 rs2255723 87_84 1: 93368309 T ENSG00000154511FAM69A FAM69A 388650 rs2811593 87_84 1: 93343891 C ENSG00000154511FAM69A FAM69A 388650 rs2811600 87_84 1: 93334138 T ENSG00000154511FAM69A FAM69A 388650 rs7514280 87_84 1: 93320869 T ENSG00000154511FAM69A FAM69A 388650 rs7536563 87_84 1: 93349046 G ENSG00000154511FAM69A FAM69A 388650 rs12411340 88_43 10: 67037492 T rs12411779 88_4310: 67038698 T rs12414755 88_43 10: 67014534 G rs17792002 88_43 10:66963409 C rs7097087 88_43 10: 67031903 G rs7912511 88_43 10: 66977696 Grs10509215 88_43 10: 66988617 A rs6497455 88_64 16: 20283920 C rs649746588_64 16: 20288797 A rs7191525 88_64 16: 20276957 G rs8050244 88_64 16:20277579 T rs8054898 88_64 16: 20290454 C rs4402575 88_64 16: 20297138 Ars11164798 88_8 1: 93172782 A ENSG00000067208 EVI5 EVI5 7813 rs134111888_8 6: 104754646 T rs1341118 88_8 6: 104754646 G rs169282 88_8 6:104765744 G rs270666 88_8 6: 104753237 C rs514235 88_8 1: 93438456 CENSG00000239710 Metazoa_SRP rs514235 88_8 1: 93438456 C ENSG00000252121U6 rs521428 88_8 1: 93445497 A ENSG00000238787 AC093577.1 rs521428 88_81: 93445497 A ENSG00000239710 Metazoa_SRP rs6571178 88_8 6: 104766876 Crs660870 88_8 1: 93445417 A ENSG00000238787 AC093577.1 rs660870 88_8 1:93445417 A ENSG00000239710 Metazoa_SRP rs7764670 88_8 6: 104774231 GENSR00001223173 rs7764670 88_8 6: 104774231 G rs9391181 88_8 6:104759143 T

Likewise, SNPs from SNP set 22_11 are located within a large intergenicregion corresponding to two overlapping and newly characterized longncRNAs AC068490.2 and AC096570.2 (Table 4). Moreover, two SNP variantsof SNP set G19_2 affect miRNA AL354928.1 and small nuclear RNA U4, aswell as protein-coding GOLGA1 gene (FIG. 6A, Table 4). Finally, the SNPsets can map to large genomic regions. That is the case with all SNPs inSNP set 22_11 (with risk of 73%), and a few in SNP set 81_13 (with riskof 95%), which correspond to two different structural CNVs alreadyannotated. These results point to accumulation of possible regulatoryalterations of gene expression pattern in these groups (Table 4), whichsuggests an underlying complex and dynamic architecture of molecularprocesses that influence vulnerability to distinct forms of SZ.

5. Bioinformatics Analysis of the SNP Set-Related Genes RevealsDisparate Molecular Consequences

A detailed analysis of SNPs and mapped genes revealed at least threecomplex scenarios affecting multiple genes in different fashions(activation, repression, antisense modulation) and producing differentmolecular consequences (Table 4). First, we determined that even asingle SNP within a SNP set could produce different consequences inaffected transcripts (Table 4). For example, one SNP from SNP set 81_13was located in a protein-coding region of the SNTG1 gene, which canproduce either a change in an intron or in a transcript affectingnonsense-mediated protein decay that would be eliminated by asurveillance pathway containing a premature stop codon (Table 4).Second, we found that multiple SNPs within a SNP set can affect multiplegenes in different ways. This heterogeneity is exemplified by SNPs fromSNP set 19_2 intersecting with both ncRNAs and the GOLGA1 gene (FIG. 4a). Third, we uncovered that multiple SNPs within different SNP sets candistinctively affect single genes. For example, SNP sets 71_55 and 146are located in different networks since they have neither SNPs norsubjects in common (FIG. 5). Yet, all SNPs within both SNP sets arelocated in the same NTRK3 gene, which influences hippocampal function,but at different locations (FIG. 6B), which thereby may modify risk forSZ differentially. Consequently it is not surprising that each SNP setis observed in different individuals with distinct phenotypicconsequences. Overall, since a single SNP can affect multiple genetranscripts, or multiple SNP sets may influence a single genetranscript, we must consider the specific transcription pathway in orderto understand antecedent mechanisms that result in equifinality andmultifinality.

6. Genes Mapped by SNP Sets at Risk Correlate with Different Aspects ofNeurodevelopment

Most genes mapped by the SNP sets are involved in neurodevelopment(Table 3). For example, the SNP set 81_13 (FIG. 5A) maps to SNTG1,PXDNL, and GP2 genes (Table 2). SNTG1 is a syntrophin that mediatesdystrophin binding in brain specifically. It is down-regulated inneurodevelopmental disorders, sleep disorders, and dementia (Table 3).PXDNL encodes a peroxidasin-like protein, which affects risk of SZ anddementia (Table 3). GP2 encodes glycoprotein 2 (zymogen granulemembrane) and is down-regulated in neuropathy and basal gangliadisorders, but up-regulated in Alzheimer's disease (Table 3).Cumulatively, characterization of all genes in terms of related diseasessupports the biological impact of these SNP sets.

TABLE 3 Mapping Genes Targeted by SNP Sets to Mental and Brain andNervous System Disorder Categories. (Information obtained fron Nextbiodatabase) Up/Down Gene Disease Score regulated 7SK Autistic disorder 39up-regulated 7SK Encephalomyelopathy 32 up-regulated 7SK Mood disorder51 down-regulated 7SK Multiple sclerosis 27 up-regulated ABCC12Alzheimer's disease 55 down-regulated ABCC12 Dementia 55 down-regulatedABCC12 Disorder of basal ganglia 2 up-regulated ABCC12 Hypoxia of brain8 up-regulated ABCC12 Meningitis 14 up-regulated ABCC12 Movementdisorder 1 up-regulated ABCC12 Multiple sclerosis 37 down-regulatedABCC12 Nerve Injury 25 down-regulated ABCC12 Neuropathy 14down-regulated ABCC12 Parkinson's disease 10 up-regulated ABCC12Psychotic disorder 47 up-regulated ABCC12 Schizophrenia 47 up-regulatedARPC5L Alzheimer's disease 26 down-regulated ARPC5L Amyotrophic lateralsclerosis 14 down-regulated ARPC5L Anxiety disorder 73 up-regulatedARPC5L Autistic disorder 45 down-regulated ARPC5L Cerebrovasculardisease 45 up-regulated ARPC5L Chronic fatigue syndrome 100down-regulated ARPC5L Dementia 26 down-regulated ARPC5L Developmentalmental 41 up-regulated disorder ARPC5L Disorder of basal ganglia 74down-regulated ARPC5L Disorder of brain 38 up-regulated ARPC5LHuntington's disease 85 down-regulated ARPC5L Meningitis 69down-regulated ARPC5L Mental retardation 38 up-regulated ARPC5L Motorneuron disease 28 up-regulated ARPC5L Movement disorder 71down-regulated ARPC5L Nerve Injury 1 down-regulated ARPC5L Parkinson'sdisease 50 down-regulated ARPC5L Prion disease 26 down-regulated ARPC5LPsychotic disorder 36 down-regulated ARPC5L Schizophrenia 36down-regulated ATP8A2 Alzheimer's disease 44 down-regulated ATP8A2Autistic disorder 23 up-regulated ATP8A2 Cerebrovascular disease 29down-regulated ATP8A2 Dementia 43 down-regulated ATP8A2 Disorder ofbasal ganglia 84 down-regulated ATP8A2 Encephalitis 46 down-regulatedATP8A2 Encephalomyelopathy 37 up-regulated ATP8A2 Huntington's disease80 down-regulated ATP8A2 Hypoxia of brain 32 down-regulated ATP8A2Meningitis 55 up-regulated ATP8A2 Movement disorder 81 down-regulatedATP8A2 Nerve Injury 31 up-regulated ATP8A2 Neuropathy 33 down-regulatedATP8A2 Parkinson's disease 84 down-regulated ATP8A2 Prion disease 40down-regulated ATP8A2 Psychotic disorder 30 0.0001 p-value ATP8A2Schizophrenia 30 0.0001 p-value ATP8A2 Sleep disorder 34 down-regulatedC14orf102 Alzheimer's disease 48 up-regulated C14orf102 Anxiety disorder17 up-regulated C14orf102 Autistic disorder 27 up-regulated C14orf102Cerebrovascular disease 20 down-regulated C14orf102 Dementia 48up-regulated C14orf102 Disorder of basal ganglia 18 up-regulatedC14orf102 Huntington's disease 24 down-regulated C14orf102 Hypoxia ofbrain 22 down-regulated C14orf102 Meningitis 51 up-regulated C14orf102Movement disorder 15 up-regulated C14orf102 Neural tube defect 42down-regulated C14orf102 Neuropathy 14 down-regulated C14orf102Parkinson's disease 8 up-regulated C14orf102 Psychotic disorder 200.0002 p-value C14orf102 Schizophrenia 21 0.0002 p-value C14orf102 Sleepdisorder 42 down-regulated C20orf78 Anxiety disorder 32 down-regulatedC20orf78 Disorder of basal ganglia 42 down-regulated C20orf78Huntington's disease 55 down-regulated C20orf78 Movement disorder 39down-regulated C20orf78 Psychotic disorder 35 up-regulated C20orf78Schizophrenia 35 up-regulated C4orf37 Autistic disorder 3 up-regulatedC4orf37 Meningitis 10 up-regulated C4orf37 Multiple sclerosis 14up-regulated C4orf37 Psychotic disorder 1 down-regulated C4orf37Schizophrenia 1 down-regulated C4orf37 Sleep disorder 16 up-regulatedC6orf138 Amnestic disorder 88 up-regulated C6orf138 Cerebrovasculardisease 48 down-regulated C6orf138 Disorder of basal ganglia 62down-regulated C6orf138 Huntington's disease 54 down-regulated C6orf138Hypoxia of brain 51 down-regulated C6orf138 Meningitis 75 down-regulatedC6orf138 Movement disorder 59 down-regulated C6orf138 Multiple sclerosis71 down-regulated C6orf138 Nerve injury 46 down-regulated C6orf138Neuropathy 83 down-regulated C6orf138 Parkinson's disease 63down-regulated CHST9 Alzheimer's disease 21 up-regulated CHST9 Amnesticdisorder 79 down-regulated CHST9 Amyotrophic lateral sclerosis 37down-regulated CHST9 Dementia 21 up-regulated CHST9 Disorder of basalganglia 33 up-regulated CHST9 Huntington's disease 47 up-regulated CHST9Meningitis 31 up-regulated CHST9 Motor neuron disease 46 down-regulatedCHST9 Movement disorder 30 up-regulated CHST9 Multiple sclerosis 56up-regulated CHST9 Nerve injury 24 down-regulated CHST9 Neuropathy 11down-regulated CHST9 Psychotic disorder 69 down-regulated CHST9Schizophrenia 69 down-regulated CSMD1 Alzheimer's disease 38 8.7E−6p-value CSMD1 Attention deficit hyperactivity 35 disorder CSMD1 Autisticdisorder 38 down-regulated CSMD1 Cerebrovascular disease 10 5.4E−5p-value CSMD1 Dementia 37 8.7E−6 p-value CSMD1 Disorder of basal ganglia49 down-regulated CSMD1 Huntington's disease 33 down-regulated CSMD1Hypoxia of brain 13 5.4E−5 p-value CSMD1 Meningitis 28 up-regulatedCSMD1 Mood disorder 38 3.6E−6 p-value CSMD1 Movement disorder 46down-regulated CSMD1 Multiple sclerosis 45 up-regulated CSMD1 Nerveinjury 23 down-regulated CSMD1 Neuropathy 29 down-regulated CSMD1Parkinson's disease 49 down-regulated CSMD1 Psychotic disorder 71down-regulated CSMD1 Schizophrenia 71 down-regulated DKK4 Autisticdisorder 33 up-regulated DKK4 Disorder of basal ganglia 1 up-regulatedDKK4 Encephalomyelopathy 3 up-regulated DKK4 Meningitis 28down-regulated DKK4 Mood disorder 43 down-regulated DKK4 Movementdisorder 1 up-regulated DKK4 Multiple sclerosis 4 up-regulated DUSP4Alzheimer's disease 1 down-regulated DUSP4 Anxiety disorder 38up-regulated DUSP4 Cerebrovascular disease 6 up-regulated DUSP4 Disorderof basal ganglia 38 down-regulated DUSP4 Disorder of brain 46down-regulated DUSP4 Encephalitis 29 up-regulated DUSP4Encephalomyelopathy 31 down-regulated DUSP4 Huntington's disease 46down-regulated DUSP4 Hypoxia of brain 16 up-regulated DUSP4 Meningitis53 up-regulated DUSP4 Mood disorder 23 down-regulated DUSP4 Movementdisorder 35 down-regulated DUSP4 Multiple sclerosis 11 down-regulatedDUSP4 Nerve injury 20 up-regulated DUSP4 Neural tube defect 29down-regulated DUSP4 Neuropathy 17 down-regulated DUSP4 Paralyticsyndrome 24 up-regulated DUSP4 Parkinson's disease 12 down-regulatedDUSP4 Psychotic disorder 22 down-regulated DUSP4 Schizophrenia 22down-regulated DUSP4 Sleep disorder 91 up-regulated DUSP4Spinocerebellar ataxia 51 down-regulated EML5 Alzheimer's disease 11down-regulated EML5 Amnestic disorder 45 up-regulated EML5 Dementia 11down-regulated EML5 Disorder of basal ganglia 66 up-regulated EML5Huntington's disease 78 up-regulated EML5 Meningitis 73 down-regulatedEML5 Movement disorder 63 up-regulated EML5 Nerve injury 77down-regulated EML5 Neuropathy 73 down-regulated EML5 Parkinson'sdisease 30 up-regulated EML5 Psychotic disorder 79 9.5E−7 p-value EML5Schizophrenia 79 9.5E−7 p-value EML5 Sleep disorder 76 down-regulatedEVI5 Amnestic disorder 65 up-regulated EVI5 Anxiety disorder 14up-regulated EVI5 Autistic disorder 29 up-regulated EVI5 Cerebral palsy17 up-regulated EVI5 Disorder of basal ganglia 34 up-regulated EVI5Huntington's disease 39 up-regulated EVI5 Meningitis 49 up-regulatedEVI5 Mood disorder 25 down-regulated EVI5 Motor neuron disease 3down-regulated EVI5 Movement disorder 31 up-regulated EVI5 Multiplesclerosis 100 6.5E−12 p-value EVI5 Nerve injury 72 up-regulated EVI5Neural tube defect 25 up-regulated EVI5 Neuropathy 4 up-regulated EVI5Parkinson's disease 23 down-regulated EVI5 Psychotic disorder 61up-regulated EVI5 Schizophrenia 62 up-regulated EVI5 Sleep disorder 42up-regulated FAM69A Alzheimer's disease 1 down-regulated FAM69A Autisticdisorder 1 down-regulated FAM69A Cerebral palsy 32 down-regulated FAM69ADementia 1 down-regulated FAM69A Disorder of basal ganglia 1up-regulated FAM69A Disorder of brain 29 up-regulated FAM69AEncephalitis 44 down-regulated FAM69A Encephalomyelitis 29down-regulated FAM69A Encephalomyelopathy 9 down-regulated FAM69AMeningitis 7 down-regulated FAM69A Mood disorder 1 down-regulated FAM69AMotor neuron disease 1 up-regulated FAM69A Movement disorder 1up-regulated FAM69A Multiple sclerosis 90 0.8E−7 p-value FAM69AMyoneural disorder 40 up-regulated FAM69A Nerve injury 17 down-regulatedFAM69A Neuropathy 11 up-regulated FAM69A Paralytic syndrome 20down-regulated FAM69A Parkinson's disease 5 up-regulated FAM69A Priondisease 6 down-regulated FAM69A Psychotic disorder 51 0.0E−6 p-valueFAM69A Schizophrenia 51 0.0E−6 p-value FAM69A Sleep disorder 39down-regulated FOXR2 Nerve injury 83 up-regulated FOXR2 Neuropathy 86up-regulated GOLGA1 Alzheimer's disease 24 0.0007 p-value GOLGA1Autistic disorder 44 down-regulated GOLGA1 Dementia 24 0.0007 p-valueGOLGA1 Disorder of basal ganglia 55 up-regulated GOLGA1 Disorder ofbrain 50 down-regulated GOLGA1 Encephalomyelopathy 51 down-regulatedGOLGA1 Huntington's disease 52 up-regulated GOLGA1 Meningitis 51down-regulated GOLGA1 Movement disorder 52 up-regulated GOLGA1 Multiplesclerosis 33 down-regulated GOLGA1 Nerve injury 66 down-regulated GOLGA1Neuropathy 35 down-regulated GOLGA1 Paralytic syndrome 61 up-regulatedGOLGA1 Parkinson's disease 55 up-regulated GOLGA1 Psychotic disorder 500.0002 p-value GOLGA1 Schizophrenia 51 0.0002 p-value GOLGA1 Sleepdisorder 91 down-regulated GP2 Alzheimer's disease 1 up-regulated GP2Amnestic disorder 20 up-regulated GP2 Anxiety disorder 1 down-regulatedGP2 Dementia 1 up-regulated GP2 Disorder of basal ganglia 1down-regulated GP2 Huntington's disease 1 down-regulated GP2 Meningitis9 down-regulated GP2 Movement disorder 1 down-regulated GP2 Nerve injury35 down-regulated GP2 Neuropathy 38 down-regulated GP2 Psychoticdisorder 12 up-regulated GP2 Schizophrenia 12 up-regulated GPR119Alzheimer's disease 59 7.8E−5 p-value GPR119 Anxiety disorder 48down-regulated GPR119 Dementia 58 7.8E−5 p-value GPR119 Nerve injury 27up-regulated GPR119 Neuropathy 29 up-regulated HACE1 Alzheimer's disease1 down-regulated HACE1 Autistic disorder 1 up-regulated HACE1Cerebrovascular disease 1 up-regulated HACE1 Dementia 1 down-regulatedHACE1 Disorder of basal ganglia 11 down-regulated HACE1 Encephalitis 1down-regulated HACE1 Huntington's disease 16 down-regulated HACE1Meningitis 3 up-regulated HACE1 Mood disorder 1 0.0003 p-value HACE1Movement disorder 8 down-regulated HACE1 Multiple sclerosis 1up-regulated HACE1 Nerve injury 6 up-regulated HACE1 Neuropathy 1down-regulated HACE1 Parkinson's disease 1 down-regulated HACE1Psychotic disorder 7 0.5E−6 p-value HACE1 Schizophrenia 7 0.5E−6 p-valueHACE1 Sleep disorder 8 up-regulated HPGDS Alzheimer's disease 37 4.0E−5p-value HPGDS Amnestic disorder 49 up-regulated HPGDS Anxiety disorder27 up-regulated HPGDS Cerebral palsy 54 up-regulated HPGDS Childhooddisorder of conduct 59 down-regulated and emotion HPGDS Dementia 374.0E−5 p-value HPGDS Disorder of basal ganglia 37 down-regulated HPGDSDisorder of brain 44 down-regulated HPGDS Huntington's disease 42down-regulated HPGDS Meningitis 23 down-regulated HPGDS Movementdisorder 34 down-regulated HPGDS Multiple sclerosis 13 up-regulatedHPGDS Nerve injury 78 up-regulated HPGDS Neuropathy 43 down-regulatedHPGDS Parkinson's disease 29 down-regulated HPGDS Prion disease 75up-regulated HPGDS Psychotic disorder 16 0.0003 p-value HPGDSSchizophrenia 16 0.0003 p-value HPGDS Sleep disorder 45 down-regulatedIGSF1 Amnestic disorder 39 up-regulated IGSF1 Autistic disorder 20up-regulated IGSF1 Disorder of basal ganglia 60 up-regulated IGSF1Disorder of brain 16 up-regulated IGSF1 Encephalitis 47 down-regulatedIGSF1 Encephalomyelopathy 20 up-regulated IGSF1 Epilepsy 14 up-regulatedIGSF1 Huntington's disease 70 up-regulated IGSF1 Meningitis 31up-regulated IGSF1 Mood disorder 6 up-regulated IGSF1 Motor neurondisease 21 up-regulated IGSF1 Movement disorder 57 up-regulated IGSF1Multiple sclerosis 1 up-regulated IGSF1 Nerve injury 48 down-regulatedIGSF1 Neuropathy 32 down-regulated IGSF1 Parkinson's disease 29down-regulated IGSF1 Psychotic disorder 17 up-regulated IGSF1Schizophrenia 18 up-regulated IGSF1 Sleep disorder 84 down-regulatedITFG1 Alzheimer's disease 44 down-regulated ITFG1 Autistic disorder 12down-regulated ITFG1 Cerebral palsy 27 up-regulated ITFG1Cerebrovascular disease 9 down-regulated ITFG1 Chronic fatigue syndrome78 up-regulated ITFG1 Dementia 43 down-regulated ITFG1 Disorder of basalganglia 78 down-regulated ITFG1 Disorder of brain 20 up-regulated ITFG1Encephalomyelopathy 21 down-regulated ITFG1 Epilepsy 8 down-regulatedITFG1 Huntington's disease 86 down-regulated ITFG1 Hypoxia of brain 2down-regulated ITFG1 Meningitis 44 up-regulated ITFG1 Mood disorder 37down-regulated ITFG1 Movement disorder 75 down-regulated ITFG1 Multiplesclerosis 24 down-regulated ITFG1 Nerve injury 28 down-regulated ITFG1Neuropathy 10 down-regulated ITFG1 Paralytic syndrome 42 down-regulatedITFG1 Parkinson's disease 62 down-regulated ITFG1 Prion disease 20down-regulated ITFG1 Psychotic disorder 22 down-regulated ITFG1Schizophrenia 23 down-regulated ITFG1 Sleep disorder 1 down-regulatedITFG1 Spinocerebellar ataxia 16 up-regulated MAGEH1 Anxiety disorder 46up-regulated MAGEH1 Autistic disorder 22 down-regulated MAGEH1 Disorderof basal ganglia 44 up-regulated MAGEH1 Encephalomyelopathy 33down-regulated MAGEH1 Huntington's disease 48 up-regulated MAGEH1Meningitis 41 up-regulated MAGEH1 Mood disorder 8 down-regulated MAGEH1Movement disorder 41 up-regulated MAGEH1 Myoneural disorder 54up-regulated MAGEH1 Nerve injury 57 down-regulated MAGEH1 Neuropathy 41up-regulated MAGEH1 Paralytic syndrome 40 up-regulated MAGEH1Parkinson's disease 36 down-regulated MAGEH1 Prion disease 30down-regulated MAGEH1 Psychotic disorder 22 down-regulated MAGEH1Schizophrenia 23 down-regulated MAGEH1 Spinocerebellar ataxia 43down-regulated NALCN Alzheimer's disease 68 down-regulated NALCNAmnestic disorder 54 down-regulated NALCN Anxiety disorder 56up-regulated NALCN Cerebrovascular disease 23 down-regulated NALCNDementia 67 down-regulated NALCN Disorder of basal ganglia 44up-regulated NALCN Epilepsy 76 3.6E−6 p-value NALCN Huntington's disease47 up-regulated NALCN Hypoxia of brain 25 down-regulated NALCNMeningitis 48 down-regulated NALCN Mood disorder 45 3.3E−5 p-value NALCNMovement disorder 41 up-regulated NALCN Multiple sclerosis 8down-regulated NALCN Myoneural disorder 39 down-regulated NALCN Nerveinjury 55 down-regulated NALCN Neuropathy 40 down-regulated NALCNParkinson's disease 39 up-regulated NALCN Prion disease 30down-regulated NALCN Psychotic disorder 51 up-regulated NALCNSchizophrenia 52 up-regulated NCAM1 Amnestic disorder 1 down-regulatedNCAM1 Autistic disorder 1 down-regulated NCAM1 Dementia 1 up-regulatedNCAM1 Disorder of basal ganglia 32 down-regulated NCAM1 Huntington'sdisease 36 up-regulated NCAM1 Meningitis 33 up-regulated NCAM1 Movementdisorder 29 down-regulated NCAM1 Parkinson's disease 23 up-regulatedNCAM1 Psychotic disorder 16 down-regulated NCAM1 Schizophrenia 17down-regulated NCAM1 Sleep disorder 11 down-regulated NETO2 Amnesticdisorder 41 down-regulated NETO2 Anxiety disorder 36 up-regulated NETO2Dementia 43 down-regulated NETO2 Disorder of basal ganglia 79down-regulated NETO2 Huntington's disease 90 down-regulated NETO2 Mooddisorder 21 down-regulated NETO2 Movement disorder 76 down-regulatedNETO2 Nerve injury 54 down-regulated NETO2 Parkinson's disease 48down-regulated NETO2 Psychotic disorder 32 up-regulated NETO2Schizophrenia 32 up-regulated NETO2 Sleep disorder 52 up-regulated NTRK3Alzheimer's disease 26 up-regulated NTRK3 Amnestic disorder 59up-regulated NTRK3 Autistic disorder 48 down-regulated NTRK3 Cerebralpalsy 65 down-regulated NTRK3 Cerebrovascular disease 33 down-regulatedNTRK3 Chronic fatigue syndrome 85 down-regulated NTRK3 Dementia 26up-regulated NTRK3 Developmental mental 50 down-regulated disorder NTRK3Disorder of basal ganglia 69 down-regulated NTRK3 Encephalitis 68down-regulated NTRK3 Huntington's disease 76 down-regulated NTRK3Hypoxia of brain 36 down-regulated NTRK3 Meningitis 80 down-regulatedNTRK3 Mental retardation 48 down-regulated NTRK3 Movement disorder 66down-regulated NTRK3 Multiple sclerosis 56 up-regulated NTRK3 Nerveinjury 91 down-regulated NTRK3 Neural tube defect 53 up-regulated NTRK3Neuropathy 68 down-regulated NTRK3 Parkinson's disease 53 down-regulatedNTRK3 Prion disease 63 up-regulated NTRK3 Psychotic disorder 94up-regulated NTRK3 Schizophrenia 94 up-regulated NTRK3 Sleep disorder 64down-regulated OPN5 Disorder of basal ganglia 27 down-regulated OPN5Meningitis 70 up-regulated OPN5 Movement disorder 24 down-regulated OPN5Neuropathy 29 down-regulated OPN5 Parkinson's disease 35 down-regulatedOPN5 Psychotic disorder 68 up-regulated OPN5 Schizophrenia 68up-regulated PAGE3 Disorder of basal ganglia 77 down-regulated PAGE3Movement disorder 74 down-regulated PAGE3 Parkinson's disease 85down-regulated PAGE5 Disorder of basal ganglia 52 down-regulated PAGE5Huntington's disease 36 down-regulated PAGE5 Meningitis 47down-regulated PAGE5 Movement disorder 49 down-regulated PAGE5 Multiplesclerosis 36 up-regulated PAGE5 Parkinson's disease 56 down-regulatedPAGE5 Psychotic disorder 86 up-regulated PAGE5 Schizophrenia 87up-regulated PHKB Alzheimer's disease 2 down-regulated PHKB Anxietydisorder 12 up-regulated PHKB Autistic disorder 7 up-regulated PHKBCerebral palsy 36 down-regulated PHKB Childhood disorder of conduct 16up-regulated and emotion PHKB Chronic fatigue syndrome 67 up-regulatedPHKB Dementia 2 down-regulated PHKB Disorder of basal ganglia 35down-regulated PHKB Disorder of brain 2 up-regulated PHKBEncephalomyelopathy 26 down-regulated PHKB Epilepsy 1 down-regulatedPHKB Huntington's disease 29 up-regulated PHKB Meningitis 35down-regulated PHKB Movement disorder 32 down-regulated PHKB Multiplesclerosis 1 down-regulated PHKB Nerve injury 25 down-regulated PHKBNeuropathy 23 down-regulated PHKB Paralytic syndrome 46 down-regulatedPHKB Parkinson's disease 36 down-regulated PHKB Prion disease 15up-regulated PHKB Sleep disorder 1 up-regulated PHKB Spinocerebellarataxia 9 up-regulated PPP1R1C Attention deficit hyperactivity 1 0.0003p-value disorder PPP1R1C Developmental mental 11 down-regulated disorderPPP1R1C Disorder of basal ganglia 1 up-regulated PPP1R1C Meningitis 8up-regulated PPP1R1C Mental retardation 9 down-regulated PPP1R1C Mooddisorder 1 0.0008 p-value PPP1R1C Movement disorder 1 up-regulatedPPP1R1C Multiple sclerosis 11 up-regulated PPP1R1C Myoneural disorder 20down-regulated PPP1R1C Nerve injury 26 up-regulated PPP1R1C Neural tubedefect 27 down-regulated PPP1R1C Neuropathy 17 down-regulated PPP1R1CParkinson's disease 1 up-regulated PPP1R1C Psychotic disorder 4 7.9E−5p-value PPP1R1C Schizophrenia 4 7.9E−5 p-value PSMC1 Alzheimer's disease41 up-regulated PSMC1 Anxiety disorder 40 up-regulated PSMC1 Autisticdisorder 23 down-regulated PSMC1 Cerebrovascular disease 54down-regulated PSMC1 Dementia 41 up-regulated PSMC1 Disorder of basalganglia 59 down-regulated PSMC1 Huntington's disease 48 down-regulatedPSMC1 Hypoxia of brain 40 up-regulated PSMC1 Movement disorder 56down-regulated PSMC1 Nerve injury 34 down-regulated PSMC1 Neuropathy 67down-regulated PSMC1 Parkinson's disease 62 down-regulated PSMC1 Priondisease 82 down-regulated PSMC1 Psychotic disorder 39 down-regulatedPSMC1 Schizophrenia 40 down-regulated PSMC1 Sleep disorder 27down-regulated PTBP2 Amnestic disorder 6 down-regulated PTBP2Amyotrophic lateral sclerosis 10 down-regulated PTBP2 Anxiety disorder45 up-regulated PTBP2 Autistic disorder 14 up-regulated PTBP2 Cerebralpalsy 28 up-regulated PTBP2 Disorder of basal ganglia 51 down-regulatedPTBP2 Encephalomyelopathy 11 down-regulated PTBP2 Epilepsy 23 0.0002p-value PTBP2 Huntington's disease 31 up-regulated PTBP2 Meningitis 51down-regulated PTBP2 Mood disorder 56 down-regulated PTBP2 Motor neurondisease 22 down-regulated PTBP2 Movement disorder 48 down-regulatedPTBP2 Nerve injury 47 down-regulated PTBP2 Neuropathy 26 down-regulatedPTBP2 Paralytic syndrome 32 up-regulated PTBP2 Parkinson's disease 57down-regulated PTBP2 Prion disease 17 down-regulated PTBP2 Psychoticdisorder 42 up-regulated PTBP2 Schizophrenia 42 up-regulated PTBP2 Sleepdisorder 1 down-regulated RP11 Amnestic disorder 30 up-regulated RP11Anxiety disorder 64 down-regulated RP11 Autistic disorder 52up-regulated RP11 Cerebrovascular disease 27 down-regulated RP11Developmental mental 68 up-regulated disorder RP11 Disorder of basalganglia 70 down-regulated RP11 Disorder of brain 49 down-regulated RP11Encephalomyelopathy 39 up-regulated RP11 Huntington's disease 82down-regulated RP11 Hypoxia of brain 24 up-regulated RP11 Meningitis 81down-regulated RP11 Mental retardation 65 up-regulated RP11 Mooddisorder 17 up-regulated RP11 Movement disorder 67 down-regulated RP11Nerve injury 25 up-regulated RP11 Neuropathy 43 up-regulated RP11Paralytic syndrome 49 up-regulated RP11 Parkinson's disease 34down-regulated RP11 Prion disease 48 down-regulated RP11 Psychoticdisorder 41 up-regulated RP11 Schizophrenia 41 up-regulated RP11 Sleepdisorder 59 down-regulated RP11 Spinocerebellar ataxia 44 up-regulatedRP13 Alzheimer's disease 51 down-regulated RP13 Attention deficithyperactivity 79 disorder RP13 Autistic disorder 68 down-regulated RP13Cerebrovascular disease 19 down-regulated RP13 Dementia 51down-regulated RP13 Developmental mental 99 disorder RP13 Disorder ofbasal ganglia 25 up-regulated RP13 Encephalitis 55 down-regulated RP13Encephalomyelopathy 24 up-regulated RP13 Huntington's disease 27up-regulated RP13 Hypoxia of brain 33 down-regulated RP13 Meningitis 71up-regulated RP13 Mental retardation 97 RP13 Movement disorder 23up-regulated RP13 Nerve injury 24 down-regulated RP13 Neuropathy 16up-regulated RP13 Paralytic syndrome 44 up-regulated RP13 Parkinson'sdisease 21 down-regulated RP13 Sleep disorder 29 down-regulated RP4Anxiety disorder 25 down-regulated RP4 Autistic disorder 25down-regulated RP4 Cerebral palsy 46 down-regulated RP4 Developmentalmental 32 down-regulated disorder RP4 Disorder of basal ganglia 8down-regulated RP4 Encephalitis 33 down-regulated RP4Encephalomyelopathy 16 up-regulated RP4 Huntington's disease 9down-regulated RP4 Meningitis 34 down-regulated RP4 Mental retardation29 down-regulated RP4 Mood disorder 36 3.1E−5 p-value RP4 Motor neurondisease 3 down-regulated RP4 Movement disorder 5 down-regulated RP4Nerve injury 31 down-regulated RP4 Neuropathy 27 down-regulated RP4Parkinson's disease 4 up-regulated RPL35 Alzheimer's disease 2up-regulated RPL35 Amnestic disorder 20 up-regulated RPL35 Autisticdisorder 30 up-regulated RPL35 Cerebrovascular disease 16 up-regulatedRPL35 Dementia 2 up-regulated RPL35 Disorder of basal ganglia 26up-regulated RPL35 Encephalitis 29 down-regulated RPL35Encephalomyelitis 40 down-regulated RPL35 Encephalomyelopathy 6down-regulated RPL35 Huntington's disease 35 up-regulated RPL35 Hypoxiaof brain 10 up-regulated RPL35 Meningitis 87 up-regulated RPL35 Mooddisorder 4 down-regulated RPL35 Motor neuron disease 23 up-regulatedRPL35 Movement disorder 23 up-regulated RPL35 Multiple sclerosis 3up-regulated RPL35 Myoneural disorder 27 up-regulated RPL35 Nerve injury26 up-regulated RPL35 Neuropathy 28 up-regulated RPL35 Parkinson'sdisease 4 down-regulated RPL35 Prion disease 15 down-regulated RPL35Psychotic disorder 1 0.0008 p-value RPL35 Schizophrenia 1 0.0008 p-valueRPL35 Sleep disorder 43 down-regulated RPL5 Alzheimer's disease 3down-regulated RPL5 Amyotrophic lateral sclerosis 29 down-regulated RPL5Autistic disorder 23 up-regulated RPL5 Cerebrovascular disease 6up-regulated RPL5 Dementia 3 down-regulated RPL5 Disorder of basalganglia 33 up-regulated RPL5 Disorder of brain 12 up-regulated RPL5Encephalitis 58 down-regulated RPL5 Encephalomyelitis 37 down-regulatedRPL5 Encephalomyelopathy 2 down-regulated RPL5 Huntington's disease 40up-regulated RPL5 Hypoxia of brain 1 up-regulated RPL5 Meningitis 52down-regulated RPL5 Motor neuron disease 38 down-regulated RPL5 Movementdisorder 30 up-regulated RPL5 Multiple sclerosis 70 2.5E−6 p-value RPL5Myoneural disorder 17 up-regulated RPL5 Nerve injury 22 down-regulatedRPL5 Neuropathy 7 up-regulated RPL5 Paralytic syndrome 17 up-regulatedRPL5 Parkinson's disease 18 up-regulated RPL5 Prion disease 13down-regulated RPL5 Psychotic disorder 54 2.2E−6 p-value RPL5Schizophrenia 55 2.2E−6 p-value RPL5 Sleep disorder 24 down-regulatedRRAGB Alzheimer's disease 22 down-regulated RRAGB Dementia 21down-regulated RRAGB Disorder of basal ganglia 36 down-regulated RRAGBDisorder of brain 17 up-regulated RRAGB Encephalitis 27 down-regulatedRRAGB Encephalomyelopathy 6 down-regulated RRAGB Huntington's disease 19down-regulated RRAGB Meningitis 11 up-regulated RRAGB Mood disorder 1up-regulated RRAGB Motor neuron disease 1 up-regulated RRAGB Movementdisorder 33 down-regulated RRAGB Multiple sclerosis 9 down-regulatedRRAGB Nerve injury 48 down-regulated RRAGB Neuropathy 6 down-regulatedRRAGB Parkinson's disease 41 down-regulated RRAGB Psychotic disorder 13down-regulated RRAGB Schizophrenia 13 down-regulated RRAGB Sleepdisorder 18 down-regulated RYR3 Alzheimer's disease 26 down-regulatedRYR3 Anxiety disorder 63 up-regulated RYR3 Autistic disorder 21up-regulated RYR3 Cerebral palsy 85 up-regulated RYR3 Cerebrovasculardisease 65 6.5E−6 p-value RYR3 Dementia 25 down-regulated RYR3Developmental mental 36 down-regulated disorder RYR3 Disorder of basalganglia 56 up-regulated RYR3 Disorder of brain 49 up-regulated RYR3Encephalitis 50 up-regulated RYR3 Encephalomyelitis 61 up-regulated RYR3Encephalomyelopathy 34 up-regulated RYR3 Epilepsy 60 0.7E−5 p-value RYR3Huntington's disease 68 up-regulated RYR3 Meningitis 57 up-regulatedRYR3 Mental retardation 34 down-regulated RYR3 Mood disorder 57 8.3E−6p-value RYR3 Movement disorder 53 up-regulated RYR3 Multiple sclerosis24 up-regulated RYR3 Myoneural disorder 46 up-regulated RYR3 Nerveinjury 70 down-regulated RYR3 Neuropathy 44 down-regulated RYR3Parkinson's disease 10 up-regulated RYR3 Prion disease 47 down-regulatedRYR3 Psychotic disorder 57 up-regulated RYR3 Schizophrenia 58up-regulated RYR3 Sleep disorder 46 up-regulated SCAI Alzheimer'sdisease 38 down-regulated SCAI Amyotrophic lateral sclerosis 41up-regulated SCAI Autistic disorder 16 up-regulated SCAI Cerebrovasculardisease 14 down-regulated SCAI Dementia 38 down-regulated SCAI Disorderof basal ganglia 77 down-regulated SCAI Huntington's disease 66down-regulated SCAI Hypoxia of brain 17 down-regulated SCAI Meningitis54 down-regulated SCAI Mood disorder 26 down-regulated SCAI Motor neurondisease 38 up-regulated SCAI Movement disorder 74 down-regulated SCAIMultiple sclerosis 3 down-regulated SCAI Nerve injury 41 up-regulatedSCAI Neuropathy 14 up-regulated SCAI Parkinson's disease 78down-regulated SCAI Prion disease 43 up-regulated SCAI Psychoticdisorder 35 down-regulated SCAI Schizophrenia 35 down-regulated SCAISleep disorder 53 up-regulated SEMA3A Alzheimer's disease 1 5.9E−5p-value SEMA3A Amnestic disorder 1 down-regulated SEMA3A Autisticdisorder 1 down-regulated SEMA3A Childhood disorder of conduct 26up-regulated and emotion SEMA3A Dementia 1 5.9E−5 p-value SEMA3ADisorder of basal ganglia 7 down-regulated SEMA3A Huntington's disease17 down-regulated SEMA3A Lissencephaly 100 SEMA3A Mood disorder 1 0.0003p-value SEMA3A Motor neuron disease 1 up-regulated SEMA3A Movementdisorder 4 down-regulated SEMA3A Multiple sclerosis 1 up-regulatedSEMA3A Nerve injury 8 up-regulated SEMA3A Neuropathy 71 down-regulatedSEMA3A Parkinson's disease 1 up-regulated SEMA3A Prion disease 45 2.7E−6p-value SEMA3A Psychotic disorder 26 down-regulated SEMA3A Schizophrenia26 down-regulated SEMA3A Sleep disorder 30 up-regulated SLC20A2 Amnesticdisorder 19 up-regulated SLC20A2 Autistic disorder 7 up-regulatedSLC20A2 Disorder of basal ganglia 28 down-regulated SLC20A2 Disorder ofbrain 26 up-regulated SLC20A2 Encephalomyelopathy 14 down-regulatedSLC20A2 Huntington's disease 29 down-regulated SLC20A2 Meningitis 8up-regulated SLC20A2 Mood disorder 19 8.5E−5 p-value SLC20A2 Motorneuron disease 5 down-regulated SLC20A2 Movement disorder 25down-regulated SLC20A2 Multiple sclerosis 50 up-regulated SLC20A2 Nerveinjury 50 up-regulated SLC20A2 Neuropathy 28 down-regulated SLC20A2Paralytic syndrome 24 down-regulated SLC20A2 Parkinson's disease 24down-regulated SLC20A2 Prion disease 40 up-regulated SLC20A2 Psychoticdisorder 17 up-regulated SLC20A2 Schizophrenia 17 up-regulated SLC20A2Sleep disorder 10 down-regulated SLC25A14 Alzheimer's disease 27down-regulated SLC25A14 Autistic disorder 1 down-regulated SLC25A14Cerebral palsy 20 down-regulated SLC25A14 Dementia 26 down-regulatedSLC25A14 Disorder of basal ganglia 45 down-regulated SLC25A14Encephalitis 24 up-regulated SLC25A14 Encephalomyelopathy 12up-regulated SLC25A14 Huntington's disease 47 down-regulated SLC25A14Meningitis 16 down-regulated SLC25A14 Movement disorder 42down-regulated SLC25A14 Multiple sclerosis 2 down-regulated SLC25A14Nerve injury 27 down-regulated SLC25A14 Neuropathy 18 down-regulatedSLC25A14 Parkinson's disease 41 down-regulated SLC25A14 Prion disease 29down-regulated SLC25A14 Psychotic disorder 25 up-regulated SLC25A14Schizophrenia 25 up-regulated SLC25A14 Spinocerebellar ataxia 14up-regulated SMARCAD1 Alzheimer's disease 19 down-regulated SMARCAD1Amnestic disorder 1 up-regulated SMARCAD1 Anxiety disorder 28up-regulated SMARCAD1 Autistic disorder 1 down-regulated SMARCAD1Cerebrovascular disease 11 up-regulated SMARCAD1 Dementia 18down-regulated SMARCAD1 Disorder of basal ganglia 1 up-regulatedSMARCAD1 Encephalomyelopathy 1 down-regulated SMARCAD1 Huntington'sdisease 11 up-regulated SMARCAD1 Meningitis 39 down-regulated SMARCAD1Mood disorder 13 up-regulated SMARCAD1 Movement disorder 1 up-regulatedSMARCAD1 Nerve injury 17 down-regulated SMARCAD1 Neuropathy 14down-regulated SMARCAD1 Paralytic syndrome 11 up-regulated SMARCAD1Prion disease 12 down-regulated SMARCAD1 Psychotic disorder 1 0.0002p-value SMARCAD1 Schizophrenia 1 0.0002 p-value SMARCAD1 Sleep disorder26 up-regulated SMARCAD1 Spinocerebellar ataxia 8 down-regulated SNORA42Attention deficit hyperactivity 90 4.9E−6 p-value disorder SNORA42Encephalomyelopathy 51 up-regulated SNORA42 Neuropathy 52 up-regulatedSNORA66 Autistic disorder 33 down-regulated SNORA66 Multiple sclerosis100 2.5E−6 p-value SNORA66 Psychotic disorder 83 2.2E−6 p-value SNORA66Schizophrenia 83 2.2E−6 p-value SNTG1 Alzheimer's disease 1down-regulated SNTG1 Cerebrovascular disease 1 down-regulated SNTG1Dementia 1 down-regulated SNTG1 Developmental mental 68 down-regulateddisorder SNTG1 Disorder of basal ganglia 30 down-regulated SNTG1Huntington's disease 38 down-regulated SNTG1 Hypoxia of brain 7down-regulated SNTG1 Meningitis 1 up-regulated SNTG1 Mental disorder 100down-regulated SNTG1 Movement disorder 27 down-regulated SNTG1 Multiplesclerosis 3 up-regulated SNTG1 Neuropathy 1 down-regulated SNTG1Parkinson's disease 13 down-regulated SNTG1 Sleep disorder 5down-regulated SNX19 Disorder of basal ganglia 49 down-regulated SNX19Encephalomyelopathy 12 down-regulated SNX19 Huntington's disease 55down-regulated SNX19 Meningitis 67 up-regulated SNX19 Mood disorder 23down-regulated SNX19 Movement disorder 46 down-regulated SNX19 Multiplesclerosis 12 down-regulated SNX19 Myoneural disorder 44 down-regulatedSNX19 Nerve injury 32 down-regulated SNX19 Neuropathy 43 down-regulatedSNX19 Paralytic syndrome 33 down-regulated SNX19 Parkinson's disease 38down-regulated SNX19 Prion disease 36 up-regulated SNX19 Psychoticdisorder 82 down-regulated SNX19 Schizophrenia 83 down-regulated SNX19Sleep disorder 51 up-regulated SOD3 Alzheimer's disease 1 down-regulatedSOD3 Anxiety disorder 1 up-regulated SOD3 Cerebrovascular disease 1down-regulated SOD3 Dementia 18 up-regulated SOD3 Disorder of basalganglia 1 up-regulated SOD3 Disorder of brain 1 down-regulated SOD3Huntington's disease 1 up-regulated SOD3 Meningitis 2 down-regulatedSOD3 Motor neuron disease 1 down-regulated SOD3 Movement disorder 1up-regulated SOD3 Nerve injury 20 up-regulated SOD3 Neuropathy 20up-regulated SOD3 Prion disease 32 up-regulated SOD3 Psychotic disorder1 up-regulated SOD3 Schizophrenia 1 up-regulated SOD3 Sleep disorder 1up-regulated SPATA7 Alzheimer's disease 23 down-regulated SPATA7Autistic disorder 39 down-regulated SPATA7 Dementia 23 down-regulatedSPATA7 Disorder of basal ganglia 71 up-regulated SPATA7 Disorder ofbrain 77 up-regulated SPATA7 Encephalomyelopathy 36 up-regulated SPATA7Huntington's disease 81 up-regulated SPATA7 Meningitis 54 up-regulatedSPATA7 Mood disorder 30 down-regulated SPATA7 Movement disorder 68up-regulated SPATA7 Nerve injury 76 down-regulated SPATA7 Neuropathy 61down-regulated SPATA7 Parkinson's disease 50 down-regulated SPATA7Psychotic disorder 75 down-regulated SPATA7 Schizophrenia 76down-regulated SPATA7 Sleep disorder 98 down-regulated ST18 Alzheimer'sdisease 63 down-regulated ST18 Amnestic disorder 37 up-regulated ST18Dementia 62 down-regulated ST18 Disorder of basal ganglia 68up-regulated ST18 Disorder of brain 69 up-regulated ST18 Epilepsy 584.8E−5 p-value ST18 Huntington's disease 76 up-regulated ST18 Mooddisorder 35 down-regulated ST18 Movement disorder 65 up-regulated ST18Multiple sclerosis 53 down-regulated ST18 Nerve injury 49 up-regulatedST18 Neuropathy 46 down-regulated ST18 Parkinson's disease 51up-regulated ST18 Prion disease 49 down-regulated ST18 Psychoticdisorder 48 up-regulated ST18 Schizophrenia 48 up-regulated ST18 Sleepdisorder 36 down-regulated STYK1 Alzheimer's disease 52 down-regulatedSTYK1 Dementia 51 down-regulated STYK1 Disorder of basal ganglia 49down-regulated STYK1 Huntington's disease 55 down-regulated STYK1Hypoxia of brain 33 up-regulated STYK1 Mood disorder 8 0.0003 p-valueSTYK1 Movement disorder 47 down-regulated STYK1 Neural tube defect 100down-regulated STYK1 Neuropathy 7 down-regulated STYK1 Parkinson'sdisease 38 down-regulated STYK1 Psychotic disorder 41 down-regulatedSTYK1 Schizophrenia 41 down-regulated TMEM135 Cerebral palsy 57up-regulated TMEM135 Dementia 24 down-regulated TMEM135 Disorder ofbasal ganglia 43 down-regulated TMEM135 Disorder of brain 44up-regulated TMEM135 Mood disorder 22 down-regulated TMEM135 Paralyticsyndrome 62 up-regulated TMEM135 Parkinson's disease 47 down-regulatedTMEM135 Psychotic disorder 54 up-regulated TMEM135 Schizophrenia 54up-regulated TRPS1 Alzheimer's disease 19 up-regulated TRPS1 Autisticdisorder 1 up-regulated TRPS1 Cerebrovascular disease 23 5.0E−5 p-valueTRPS1 Dementia 18 up-regulated TRPS1 Disorder of basal ganglia 57up-regulated TRPS1 Encephalomyelopathy 1 down-regulated TRPS1Huntington's disease 66 up-regulated TRPS1 Hypoxia of brain 14up-regulated TRPS1 Meningitis 51 up-regulated TRPS1 Mood disorder 10.0004 p-value TRPS1 Motor neuron disease 13 down-regulated TRPS1Movement disorder 54 up-regulated TRPS1 Multiple sclerosis 27up-regulated TRPS1 Nerve injury 27 up-regulated TRPS1 Neuropathy 29up-regulated TRPS1 Parkinson's disease 36 up-regulated TRPS1 Psychoticdisorder 18 up-regulated TRPS1 Schizophrenia 18 up-regulated TRPS1 Sleepdisorder 15 down-regulated TRPS1 Spinocerebellar ataxia 12down-regulated VANGL1 Autistic disorder 1 down-regulated VANGL1 Disorderof basal ganglia 1 up-regulated VANGL1 Epilepsy 11 down-regulated VANGL1Huntington's disease 1 up-regulated VANGL1 Meningitis 1 up-regulatedVANGL1 Mood disorder 1 down-regulated VANGL1 Neural tube defect 100VANGL1 Psychotic disorder 1 down-regulated VANGL1 Schizophrenia 1down-regulated VDAC3 Anxiety disorder 27 up-regulated VDAC3 Autisticdisorder 18 up-regulated VDAC3 Dementia 20 down-regulated VDAC3 Disorderof basal ganglia 48 down-regulated VDAC3 Encephalomyelopathy 50down-regulated VDAC3 Meningitis 65 up-regulated VDAC3 Myoneural disorder56 up-regulated VDAC3 Parkinson's disease 53 down-regulated WDR38Disorder of basal ganglia 41 up-regulated WDR38 Huntington's disease 54up-regulated WDR38 Meningitis 38 up-regulated WDR38 Movement disorder 38up-regulated WDR38 Multiple sclerosis 40 up-regulated WDR38 Nerve injury75 up-regulated WDR38 Neuropathy 64 up-regulated WDR38 Psychoticdisorder 54 down-regulated WDR38 Schizophrenia 54 down-regulated ZC3H14Alzheimer's disease 9 up-regulated ZC3H14 Amyotrophic lateral sclerosis33 down-regulated ZC3H14 Anxiety disorder 43 up-regulated ZC3H14Autistic disorder 16 up-regulated ZC3H14 Cerebrovascular disease 29up-regulated ZC3H14 Dementia 8 up-regulated ZC3H14 Disorder of basalganglia 59 up-regulated ZC3H14 Disorder of brain 16 down-regulatedZC3H14 Encephalitis 41 down-regulated ZC3H14 Encephalomyelitis 52down-regulated ZC3H14 Encephalomyelopathy 18 down-regulated ZC3H14Huntington's disease 63 up-regulated ZC3H14 Meningitis 51 down-regulatedZC3H14 Mood disorder 25 down-regulated ZC3H14 Motor neuron disease 30down-regulated ZC3H14 Movement disorder 56 up-regulated ZC3H14 Multiplesclerosis 57 down-regulated ZC3H14 Myoneural disorder 49 up-regulatedZC3H14 Nerve injury 24 down-regulated ZC3H14 Neuropathy 32down-regulated ZC3H14 Paralytic syndrome 41 up-regulated ZC3H14Parkinson's disease 53 up-regulated ZC3H14 Prion disease 43 up-regulatedZC3H14 Psychotic disorder 37 down-regulated ZC3H14 Schizophrenia 38down-regulated ZC3H14 Sleep disorder 68 down-regulated

Pathways

We identified distinct pathways (see Tables 2 and 6, and FIG. 7)including genes that have already been reported as associated with SZ byGWAS, as well as genes known to be abnormally expressed in the brain ofSZ patients. Overall, the products of genes uncovered by the SNP setsare included in several well-known, relevant and interconnectedsignaling pathways. Annotation information was manually curated andobtained from the Haploreg DB and from the Ensembl and NCBI webservices.

PI3K/Akt Signaling.

Akt is a Serine/threonine Kinase, it is activated by tyrosine kinasereceptors, integrins, T and B cell receptors, cytokine receptors,G-proteins-coupled receptors and other stimuli that involves theproduction of PIP3 triphosphate (phosphatidylinositol triphosphate) byPI3K (phosphoinositide 3 kinase). PI3K can be activated by differentways:

FOXR2 (forkhead box R2) is a proto-oncogene when it is mutated,maintained cell growth and proliferation through activation of RAS(GTPase) increase aberrant signaling through pathways PI3K/AKT/mTOR andRAS/MAP/ERK, inhibiting apoptosis.

SOD3 (superoxide dismutase 3) causes increased of phosphorylation ofERK/Ras and PIP3 because PI3K, SOD3 may be Phosphorilated by Erk½.

SEMA3A inhibits the proliferation and cell growth in neurons andprevents axonal growth by inhibiting the PI3K/Akt via inhibition of Ras.Neuropilin and SEMA1 bound active apoptosis via PI3K/Akt.

RAS (GTPase) can be activated by FOXR2 mutated by SOD3 and inhibited bySema3A. Ras and PI3K can activate mTORC1 by cRaf/MEK/ERK.

SNX19 inhibits Akt phosphorylation resulting in apoptosis.

STYK1 oncogene that binds to Akt to activate the cascade signalingdownstream and leading to increased tumor cells and increasing the riskof metastasis.

CHST9 catalyzes the sulfates transfer to N-acetylgalactosamine residues,inhibits Cd19/p85/PI3K-p110 complex.

RRAGB is part of RAG proteins that interact with mTORC1 family and arerequired for activation of amino acids via mTORC1.

Signaling Pathways Activating MAPK/p38/p53.

p38 MAPKs (.alpha., .beta, .gamma., and .delta.) are members of the MAPKfamily that are activated by a variety of environmental stresses andinflammatory cytokines. As with other MAPK cascades, themembrane-proximal component is a MAPKKK, typically a MEKK or a mixedlineage kinase (MLK). The MAPKKK phosphorylates and activates MKK3/6,the p38 MAPK kinases. MKK3/6 can also be activated directly by ASK1,which is stimulated by apoptotic stimuli. p38 MAPK is involved inregulation of HSP27, MAPKAPK-2 (MK2), MAPKAPK-3 (MK3), and severaltranscription factors including ATF-2, Statl, the Max/Myc complex,MEF-2, Elk-1, and indirectly CREB via activation of MSK1. This pathwaymay be activated by activation of PI3K way Rac/MEK/ERK.

DUSP4 is a MKP able of inhibiting p38MAPK 12 and 14a, is regulated byTNF-α expression. Decreases ERK ½ and reducing the cellular viability byalteration of the NF-.kappa.B/MAPK pathways.

MAGEH1 expression causes apoptosis of melanoma cells through theinteraction with the inner region to the membrane of the p75neurotrophin receptor (p75NTR) one TNF receptor type, and possibly alsothrough competition with the TNF receptor associated factor-6 (TRAF6)and catalytic neurotrophin receptor (TRK) for the same site ofinteraction with p75.

Nucleus

TRPS1 The gene encodes for an atypical member of the GATA family. It canactivate Snail 1 to produce inhibition of cadherines inside of nucleus.

ST18 is a promoter of hypermethylation, ST18 loss of expression in tumorcells suggests that this epigenetic mechanism responsible for thespecific down-regulation of tumor.

SPATA7 may be involved in the preparation of chromatin in early meioticprophase in the nuclei for the initiation of meiotic recombination.

ZC3H14 a protein with zinc finger Cys3His evolutionarily conserved thatspecifically binds to RNA and polyadenosine therefore postulated tomodulate post-transcriptional gene expression.

U4, is part of snRNP small nucleolar ribonucleic particles(RNA-protein), each one bind specifically to individual RNA. Thefunction of the human U4 3″SL micro RNA is unclear. It exists to enablethe formation of nucleoplasm in Cajal bodies.

PPP1R1C (Protein phosphatase 1, regulatory subunit 1C) is aprotein-coding gene and inhibitor of PP 1, and is itself regulated byphosphorylation. It promotes cell growth and may protect against celldeath, particularly when induced by pathological stress.

PRPF31 main function is thought to recruit and strap for U4/U6 U5tri-snRNP.

EVI5 works in G1/S phases, prevents phosphorylation of Emi 1 by Plk1 andtherefore inactive APC/C and accumulates cyclin A. In prometaphase, Plk1phosphorylates to EVI5, producing its inactivation and subsequentactivation of APC/C and downstream signaling pathways to complete themitotic cycle.

SNORA42: The main functions of snoRNAs has long been thought to modify,mature and stabilize rRNAs. These posttranslationalmodifications-transcriptional are important for production of accurateand efficient ribosome. Moreover, some snoRNAs are processed to producesmall RNAs.

SNORD112. SnoRNAs act as small nucleolar ribonucleoproteins (snoRNPs),each of which consists of a C/D box or box H/ACA RNA guide, and four C/Dand H/ACA snoRNP associated proteins. In both cases, snoRNAsspecifically hybridize to the complementary sequence in the RNA, andprotein complexes associated then perform the appropriate modificationto the nucleotide that is identified by the snoRNAs.

SMARCAD1 contributes as part of a large complex with HDAC1, HDAC2, andKAP1 G9A to integrate with nucleosome spacing and histone deacetylation.H3K9 methylation is required for heterochromatin restore apparentlyfacilitates histone deacetylation and H3K9mc3. How chromatin remodelingis done by deacetylation is unknown, but it seems to coordinate spacingbetween nucleosomes with H3K9 acetylation and monomethylation.

Mitochondria

SLC25A14 uncoupling protein that facilitates the transfer of anions fromthe inside of the mitochondria to the outer mitochondrial membrane andthe return transfer of protons from the outside to the innermitochondrial membrane. SLC25A14 functional role in cellular energysupply and the production of superoxide after it overexpressed inneuronal cells. In untreated culture conditions, overexpression of MMPand SLC25A14 significantly decreased content of intracellular ATP.

TMEM135, some studies have demonstrated TMEM135 association withmitochondrial's fat metabolism, and a possible role for TMEM135 recentlyidentified in improving fat storage.

VDAC3 selective Anions voltage-dependent channels (VDACs) are proteinsthat form pores allowing permeability of the mitochondrial outermembrane. A growing body of evidence indicates that VDAC plays a majorrole in metabolite flow in and out of mitochondria, resulting inregulation of mitochondrial functions.

Membrane

SLC20A2 the proteins of this group transport stream comprises an initialjoining of a Na+ion, followed by a random interaction between Pi(inorganic phosphorus) monovalent and second ion Na+. Reorientationloaded carrier, then leads to the release substrate in the cytosol.

NALCN encoding a voltage-independent, cationic, non-selective,non-inactivating, permeable to sodium, potassium and calcium channelwhen expressed exogenously in HEK293 cells. Sodium is important forneuronal excitability in vivo, the NALCN channel seems to be the mainsource of sodium leak in hippocampal neurons and because these twoprocesses are strongly altered in schizophrenia is the hypothesis had toNALCN could show a genetic association with schizophrenia.

HACE1 is a tumor suppressor, catalyses poly-Rac1 ubiquitylation atlysine 147 upon activation by HGF, resulting in its proteasomaldegradation. HACE1 controls NADPH oxidase. HACE1 promotes increasedbinding to Rac1 regulating the NADPH oxidase, decrease the production ofoxygen free radicals, and inhibit the expression of cyclin D1 anddecrease susceptibility to damage DNA. HACE1 loss leads to overactiveNADPH oxidase, increased ROS generation, also the expression of cyclinD1 and DNA damage induced by ROS.

NCAM1 is a constitutive molecule expressed on the surface of variouscells, promotes neurite outgrowth, nerve branching, fasciculation andcell migration.

OPN5 apparent gabaergic interaction in Synaptic space.

NETO2 is an auxiliary subunit determines the functional propiedadde KARSproteins (kainate, a subfamily of ionotropic glutamatereceptors—iGluRs—) that mediate excitatory synaptic transmission,regulate the release of neurotransmitters and in selective distributionin brain.

VANGL1 This gene encodes a member of the family tretraspanin. Mutationsin this gene are associated with neural tube defects. Alternativesplicing results in multiple transcript variants.

DKK4 is a DKK to block the expression of LRP and thus union with thecomplex Frizzled and Wnt/SFRP/WIF blocking the release of b-catenin.

NTRK3 is a member of the family of neurotrophin receptors and iscritical for the development of the nervous system. Published studiessuggested that NTRK3 is a dependence receptor, which signals both theligand-bound state (“on”) and the free ligand (“off”) state (see chart).When present the ligand neurotrophin-3 (NT-3), NTRK3 trigger signalswithin the cell via a tyrosine kinase domain in promoting cellproliferation and survival. In the absence of NT-3, NTRK3 signals forcell death by triggering apoptosis. Therefore, NTRK3 have the potentialto be an oncogene or tumor suppressor gene function of the presence ofNT-3.

Reticular Endoplasmic Reticulum

PSMC1 is involved in the destruction of the protein in bulk at a fast orslow rate in a wide variety of biological processes such as cell cycleprogression, apoptosis, regulation of metabolism, signal transduction,and antigen processing.

PTBP2 Ptbp1 and Ptbp2 regulate the alternative splicing of various RNAtarget assemblies, suggesting that the roles of Ptbp½ proteins aredifferent in different cellular contexts. Ptbp2 functions in the brainare not clear.

RyR3s is a type of ion channel that intracellular free Ca2+ when openedfrom the endoplasmic reticulum (ER). It is very similar to the inositoltriphosphate receptor (inositol-1,4,5-triphosphate) IP3R. The mainsignal to trigger the opening of RyRs are Ca2+ has usually enteredthrough voltage-dependent channels of cell membrane. RyR3 is expressedin several cell types including the brain in small quantities, RyR3deficient mice have impaired hippocampal synaptic plasticity andimpaired learning. ATP also stimulates the activity of the channelsRyR3. The therapeutic targets focus on molecules that induce releasecontrol, internalization and calcium mobilization.

RPL35 is a protein binding to the signal recognition particle (SPR) andits receptor (SR). They mediate targeting complexes nascentchain-ribosome to the endoplasmic reticulum.

RPL5 is an MDM2 binding protein (MDM2 oncogene, protein E3 ubiquitinligase) and SRSF 1 (serine/rich splicing factor arginine 1) to stabilizep53 oncogene and to induce cell senescence. RPL can join RPL11 and otherribosomal proteins to silence Hdm2 and p53.

FAM69A calico dependent kinase, extracellular and intracellular,localized in the endoplasmic reticulum.

Other Organelles

GOLGA1 is part transport proteins of the Golgi apparatus, whichparticipates in glycosylation and transport of proteins and lipids inthe secretory pathway.

EMLS blocks EMAP via MAP or stabilization of microtubules.

ARPC5L component can function as Arp⅔ complex which is involved in theregulation of actin polymerization and together with the activation offactor inducing nucleation (NPF) mediates the formation of branchednetworks of actin. It belongs to the family Arpc5.

CSMD1 in the TGF-.beta. pathway, CSMD1 permits the TGF-.beta. receptor Ijunction, allowing it to phosphorylate Smad3 and thus allow complexformation: phosphorylated Smad3/phosphorylated Smad2/Smad4; the complexis internalized into the cellular nucleus and bound to a transformingfactor leads to apoptosis. In addition, the TGF-.beta. receptor II bindsthe phosphorylated complex, allowing for subsequent binding Smad1/5/8with Smad4, and nuclear internalizing inducing apoptosis mediated bybinding to a transforming factor.

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What is claimed is:
 1. A method of predicting schizophrenia type in asubject having schizophrenia, comprising: obtaining a biological samplefrom a subject comprising DNA (e.g., plasma or tissue extracts);detecting by genome array, low density PCR array or oligo array singlenucleotide polymorphisms (SNPs) consisting of 19_2, 88_64, 81_13, 87_76,58_29, 83_41, 9_9, 10_4, 14_6, 56_30, 42_37, 65_25, 71_55, 12_11, 90_78,77_5, 88_8, 51_28, 59_48, 41_12, 22_11, 13_12, 31_22, 85_84, 87_84,16_10, 56_19, 75_31, 81_73, 85_23, 21_8, 76_74, 61_39, 75_67, 76_63,81_3, 87_26, 88_43, 25_10, 12_2, 52_42, and 54_51; and assigning thesubject to a schizophrenia type selected from (i) severe process, withpositive and negative symptom schizophrenia; (ii) positive and negativeschizophrenia; (iii) negative schizophrenia; (iv) positiveschizophrenia; (v) severe process, positive schizophrenia; (vi) moderateprocess, disorganized negative schizophrenia; (vii) moderate process,positive and negative schizophrenia; or (viii) moderate process,continuous positive schizophrenia.
 2. The method of claim 1, wherein theone or more SNP sets are selected from the group consisting of 88_8,90_78, 65_25, 42_37, 71_55, 56_30, 77_5, 12_11, 51_28, 59_48, 10_4,83_41, 58_29, 9_9, 14_6, 87_76, 88_64, and 81_13.
 3. The method of claim1, wherein the one or more SNP sets are selected from the groupconsisting of 10_4, 83_41, 58_29, 9_9, 14_6, 87_76, 88_64, and 81_13. 4.The method of claim 1, wherein the one or more SNP sets are selectedfrom the group consisting of 87_76, 88_64, and 81_13.
 5. The method ofclaim 1, wherein the system selects for severe process, with positiveand negative symptom schizophrenia, and wherein the one or more SNP setscomprise 56_30, 75_67, or 76_74.
 6. The method of claim 1, wherein thesystem selects for positive and negative Schizophrenia, and wherein theone or more SNP sets comprise 59_48, 71_55, 21_8, 54_51, 31_22, 65_25,or 87_84.
 7. The method of claim 1, wherein the system selects fornegative Schizophrenia, and wherein the one or more SNP sets comprise58_29, 9_9, 22_11, 81_3, 13_12, 61_39, 10_4, 81_73, 75_31, 56_19, 88_8,or 12_2.
 8. The method of claim 1, wherein the system selects forPositive Schizophrenia, and wherein the one or more SNP sets comprise88_64, 85_84, or 41_12.
 9. The diagnostic system of claim 1, wherein thesystem selects for severe process, positive schizophrenia, and whereinthe one or more SNP sets comprise 77_5, 81_13, or 25_10.
 10. The methodof claim 1, wherein the system selects for moderate process,disorganized negative schizophrenia, and wherein the one or more SNPsets comprise 19_2, 52_42, 90_78, 12_11, 87_76, or 14_6.
 11. The methodof claim 1, wherein the system selects for moderate process, positiveand negative schizophrenia, and wherein the one or more SNP setscomprise 42_37, 88_43, or 51_28.
 12. The method of claim 1, wherein thesystem selects for moderate process, continuous positive schizophrenia,and wherein the one or more SNP sets comprise 16_10, 83_41, or 87_26.13. The method of claim 1, further comprising one or more phenotypepanels, wherein each phenotype panel comprises one or more phenotypicsets selected from the group comprising 15_13, 12_11, 21_1, 50_46, 9_6,46_23, 54_11, 30_17, 18_13, 27_6, 61_18, 64_11, 65_64, 12_4, 42_9,52_28, 7_3, 48_41, 26_8, 69_41, 10_5, 17_2, 63_24, 69_66, 22_13, 53_6,59_41, 20_19, 55_7, 34_17, 27_7, 4_1, 66_54, 8_4, 51_38, 42_7, 18_3,46_29, 5_2, 57_39, 11_5, 24_4, 48_7, 28_23, or 25_20.
 14. The method ofclaim 13, wherein the system selects for severe process, with positiveand negative symptom schizophrenia, and wherein the one or morephenotypic sets comprise 15_13, 12_11, 21_1, 50_46, 9_6, 46_23, 54_11,30_17, 18_13, 27_6, 61_18, 64_11, or 65_64.
 15. The method of claim 13,wherein the system selects for positive and negative schizophrenia, andwherein the one or more phenotypic sets comprise 12_4 or 42_9.
 16. Thediagnostic system of claim 14, wherein the system selects for negativeschizophrenia, and wherein the one or more phenotypic sets comprise52_28, 7_3, 48_41, 26_8, 69_41, 10_5, or 17_2.
 17. The diagnostic systemof claim 14, wherein the system selects for positive schizophrenia, andwherein the one or more phenotypic sets comprise 63_24 and 69_66. 18.The diagnostic system of claim 14, wherein the system selects for severeprocess, positive schizophrenia, and wherein the one or more phenotypicsets comprise 22_13, 18_13, 53_6, 59_41, 20_19, 55_7, 34_17, 69_66,27_7, 18_13, 4_1, 66_54, or 8_4.
 19. The method of claim 13, wherein thesystem selects for moderate process, disorganized negativeschizophrenia, and wherein the one or more phenotypic sets comprise51_38, 42_7, 18_3, or 46_29.
 20. The method of claim 13, wherein thesystem selects for moderate process, positive and negativeschizophrenia, and wherein the one or more phenotypic sets comprise 5_2,57_39, 11_5, or 24_4.
 21. The method of claim 13, wherein the systemselects for moderate process, continuous positive schizophrenia, andwherein the one or more phenotypic sets comprise 48_7, 28_23, or 25_20.22. The method of claim 1, wherein the method further comprises a meansfor reading the one or more SNP sets, a computer operationally linked tothe means for reading the one or more SNP sets, and a display forvisualizing the diagnostic risk; wherein the computer identifies theSNP, compares the SNP profile to a control, and catalogs that data,wherein the computer provides an input source for inputting phenotypicdata into a phenomic database; wherein the computer compares the SNP andphenotypic data and calculates relationships between the genomic andphenotypic data; wherein the computer compares the genomic andphenotypic relationship data to a reference standard; and wherein thecomputer outputs the relationship data and the standard on the display.23. A method of diagnosing a subject with schizophrenia comprisingobtaining a biological sample from the subject, obtaining clinical datafrom the subject, and applying the biological sample and clinical datato the diagnostic system of claim
 1. 24. A method of diagnosing asubject with schizophrenia and determining the schizophrenia classcomprising: a. obtaining a biological sample from the subject; b.obtaining clinical data from the subject; c. applying the biologicalsample and clinical data to a diagnostic system for diagnosingschizophrenia, wherein the diagnostic system comprises one or moreexpression panels and one or more phenotypic panels; d. comparing thegenomic and phenotypic panels results to a reference standard; whereinthe presence of one or more SNP sets and phenotypic sets in the subjectssample indicates the presence of schizophrenia, and wherein the genomicand phenotypic profile of the reference standard most closelycorrelating with the subjects genomic and phenotypic profile indicatesschizophrenia class of the subject.
 25. The method of claim 23, whereinthe one or more expression panels each comprise one or more of thesingle nucleotide polymorphism (SNP) sets selected from the groupcomprising 19_2, 88_64, 81_13, 87_76, 58_29, 83_41, 9_9, 10_4, 14_6,56_30, 42_37, 65_25, 71_55, 12_11, 90_78, 77_5, 88_8, 51_28, 59_48,41_12, 22_11, 13_12, 31_22, 85_84, 87_84, 16_10, 56_19, 75_31, 81_73,85_23, 21_8, 76_74, 61_39, 75_67, 76_63, 81_3, 87_26, 88_43, 25_10,12_2, 52_42, or 54_51.
 26. The method of claim 23, wherein the one ormore phenotype panels each comprise one or more phenotypic sets selectedfrom the group comprising 15_13, 12_11, 21_1, 50_46, 9_6, 46_23, 54_11,30_17, 18_13, 27_6, 61_18, 64_11, 65_64, 12_4, 42_9, 52_28, 7_3, 48_41,26_8, 69_41, 10_5, 17_2, 63_24, 69_66, 22_13, 53_6, 59_41, 20_19, 55_7,34_17, 27_7, 4_1, 66_54, 8_4, 51_38, 42_7, 18_3, 46_29, 5_2, 57_39,11_5, 24_4, 48_7, 28_23, or 25_20.